No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Field-based phenomics for plant genetics research
Abstract
A major challenge for crop research in the 21st century is how to predict crop performance as a function of genetic architecture. Advances in “next generation” DNA sequencing have greatly improved genotyping efficiency and reduced genotyping costs. Methods for characterizing plant traits (phenotypes), however, have much progressed more slowly over the past 30 years, and constraints in phenotyping capability limit our ability to dissect the genetics of quantitative traits, especially those related to harvestable yield and stress tolerance. As a case in point, mapping populations for major crops may consist of 20 or more families, each represented by as many as 200 lines, necessitating field trials with over 20,000 plots at a single location. Investing in the resources and labor needed to quantify even a few agronomic traits for linkage with genetic markers in such massive populations is currently impractical for most breeding programs. Herein, we define key criteria, experimental approaches, equipment and data analysis tools required for robust, high-throughput field-based phenotyping (FBP). The focus is on simultaneous proximal sensing for spectral reflectance, canopy temperature, and plant architecture where a vehicle carrying replicated sets of sensors records data on multiple plots, with the potential to record data throughout the crop life cycle. The potential to assess traits, such as adaptations to water deficits or acute heat stress, several times during a single diurnal cycle is especially valuable for quantifying stress recovery. Simulation modeling and related tools can help estimate physiological traits such as canopy conductance and rooting capacity. Many of the underlying techniques and requisite instruments are available and in use for precision crop management. Further innovations are required to better integrate the functions of multiple instruments and to ensure efficient, robust analysis of the large volumes of data that are anticipated. A complement to the core proximal sensing is high-throughput phenotyping of specific traits such as nutrient status, seed composition, and other biochemical characteristics, as well as underground root architecture. The ability to “ground truth” results with conventional measurements is also necessary. The development of new sensors and imaging systems undoubtedly will continue to improve our ability to phenotype very large experiments or breeding nurseries, with the core FBP abilities achievable through strong interdisciplinary efforts that assemble and adapt existing technologies in novel ways.
... Higher yielding, stress resilient crop varieties are a necessity to feed an ever increasing population and overcome the effects of climate change [1][2][3]. This will only be achieved by a synergy of plant breeding with multiple "omic" sciences. ...
... This will only be achieved by a synergy of plant breeding with multiple "omic" sciences. While the advances in sequencing technology have made a plant genome increasingly accessible to science, our capacity to physically describe a plant's architecture and physiology has not kept pace [3]. Phenomics assesses plant growth, in a given environment, over time [2]. ...
... Field-based, proximal phenomics instruments are used to collect data on crop variety performance under varying environmental conditions [3]. Data collection must be rapid, repeatable, and precise to reliably capture crop changes over time. ...
Future crop varieties must be higher yielding, stress resilient and climate agile to feed a larger population, and overcome the effects of climate change. This will only be achieved by a fusion of plant breeding with multiple “omic” sciences. Field-based, proximal phenomics assesses plant growth and responses to stress and agronomic treatments, in a given environment, over time and requires instruments capable of capturing data, quickly and reliably. We designed the PlotCam following the concepts of cost effective phenomics, being low-cost, light-weight (6.8 kg in total) and portable with rapid and repeatable data collection at high spatial resolution. The platform consisted of a telescoping, square carbon fiber unipod, which allowed for data collection from many heights. A folding arm held the sensor head at the nadir position over the plot, and an accelerometer in the arm ensured the sensor head was level at the time of data acquisition. A computer mounted on the unipod ran custom software for data collection. RGB images were taken with an 18 MP, WiFi controlled camera, infrared thermography data was captured with a 0.3 MP infrared camera, and canopy height measured with a 0.3 MP stereo depth camera. Incoming light and air temperature were logged with every image. New operators were quickly trained to gather reliable and repeatable data and an experienced operator could image up to 300 plots per hour. The PlotCam platform was not limited by field design or topography. Multiple identical PlotCams permitted the study of larger populations generating phenomic information useful in variety improvement. We present examples of data collected with the PlotCam over field soybean experiments to show the effectiveness of the platform.
... Large-scale phenotyping and the power of a phenomic study A common association with phenomics is large-scale phenotyping. In fact, some authors have matched the concept of phenomics to high-throughput phenotyping [7,32,33]. As we explained in previous sections, however, phenomics is more than just a method, a technique, or a technology. ...
... Researchers have recognized the benefits of phenomics for plant breeding [7,10,[32][33][34][35] since phenomics can be used in conjunction with other omics [4,32,36,37]. It has also been concluded that the ultimate goal of plant phenomics is to close the gap between genomics and agricultural sciences [10,35,38]. ...
Phenomics is a relatively new discipline of biology that has been widely applied in several fields, mainly in crop sciences. We reviewed the concepts used in this discipline (particularly for plants) and found a lack of consensus on what defines a phenomic study. Furthermore, phenomics has been primarily developed around its technical aspects (operationalization), while the conceptual framework of the actual research lags behind. Each research group has given its own interpretation of this 'omic' and thus unwittingly created a 'conceptual controversy'. Addressing this issue is of particular importance, as the experimental designs and concepts of phenomics are so diverse that it is difficult to compare studies. In this opinion article, we evaluate the conceptual framework of phenomics.
... Fifty-four spinach landraces collected from diverse geographical regions of Iran were evaluated for ten (10) qualitative and nine (9) quantitative characters [1]. Divergence of forty-four spinach were estimated using twenty-one (21) morphological characters by [16]. Variation were observed in respect of different morphological traits which shown the possibility to get desirable plant characters in selective accessions, in order to fulfill the demand of a plant breeder [8]. ...
... Cluster analysis is a tool for classifying the materials into groups. On the basis of multiple variable grouping of a large number of accessions, is a reliable technique to determine the similarities and extent of differences among them [21]. The cluster analysis indicates the extent of variability that could be useful for future breeding programs [22]. ...
... Next-generation high-throughput phenotyping involves gathering enormous amounts of picture data, storing it safely, working quickly and efficiently, performing time-and money-saving analyses, and dissecting objective data (without influence of human perception). Abiotic and biotic stresses, adaptation to abiotic and biotic limiting conditions, metabolomics traits and quality traits, physiological traits, and plant structure and morphogenetic traits are among the high-throughput phenotyping spectral traits that are appropriate for biodiversity conservation and genetic resource preservation (White et al., 2012;Yang et al., 2017;Jimenez-Berni et al., 2018). The crop canopy temperature may be swiftly and non-destructively acquired using next-generation phenomobiles equipped with infrared thermal imagers, which can efficiently discern temperature variations in the crop canopy under various environmental conditions. ...
... As a result of its strong relationship to cell structure, the reflectance of plant leaves in the near-infrared (NIR) band can be used to assess a number of spectral properties, including ground canopy cover and physiological traits of plants (Espina et al., 2018;Jimenez-Berni et al., 2018). Using plant cell structures based on the latest phenotyping methods and platforms, such as phenomobiles, biodiversity and genetic resources could be verified, tracked, or conserved (White et al., 2012). The crop leaves' different spectral band absorption and reflection properties-high absorption in the visible band and significant reflection in the near-infrared band-provide the physical underpinnings for remote sensing crop growth monitoring. ...
Biodiversity and genetic resources could be preserved via phenotyping and monitoring technology. Satellite, aerial, and close-range techniques are among these technologies, as well as spectrum laboratories and phenomics. In this study, enhanced generation phenotyping techniques for protecting plant genetic diversity and resources were examined. Images from satellites, manned planes, and unmanned planes are frequently used for phenotyping. These photographs often lack the fine features required for the protection of biodiversity and genetic resources because to their low spatial resolution (in the context of both in situ and ex situ conservation), poor sensitivity when it is cloudy, delayed data transmission, and expensive expenses. For phenotyping, many depth scales can be used, such as high or low resolution and high or low throughput volumes. Since high-throughput methods frequently analyze the entire plant at medium-low resolution, they are suitable for preserving biodiversity and enhancing genetic resolution. Automated systems could nondestructively screen hundreds of accessions per day if it were used in ex situ and in situ fields for phenotyping and crop monitoring. Given that the fusion of phenomics and genomics has the potential to revolutionize plant breeding, this high throughput method can be used to conserve genetic resources and biodiversity. High throughput phenotyping, which combines increasingly potent and thorough sensors and cameras with large-scale and inexpensive phenotype difference manufacturing, is the driving force behind phenomics. Plant phenomics tries to characterize every possible phenotypic for a specific genotype under different environmental circumstances. Therefore, phenomics includes structural, physiological, and performance-related characteristics and requires phenotyping at several organizational levels (from cellular components to entire plants and canopy). Even though they are efficient, modern plant phenotyping platforms, such phenotowers, blimps, and phenomobiles with GPS navigation and sensors, are quite expensive in terms of investments, data management, and the need for skilled personnel.
... Fifty-four spinach landraces collected from diverse geographical regions of Iran were evaluated for ten (10) qualitative and nine (9) quantitative characters [1]. Divergence of forty-four spinach were estimated using twenty-one (21) morphological characters by [16]. Variation were observed in respect of different morphological traits which shown the possibility to get desirable plant characters in selective accessions, in order to fulfill the demand of a plant breeder [8]. ...
... Cluster analysis is a tool for classifying the materials into groups. On the basis of multiple variable grouping of a large number of accessions, is a reliable technique to determine the similarities and extent of differences among them [21]. The cluster analysis indicates the extent of variability that could be useful for future breeding programs [22]. ...
Spinach is a leafy green vegetable cultivated in most part of the world due to its nutritive and medicinal value. Fifty-one germplasm of spinach (Spinacia oleracea L.) collected from diverse geographical region of Bangladesh were evaluated to assess their diversity using several qualitative and quantitative characters. Variation was found in all the characters except Inflorescence color and seed type. Leaf shape was exhibited as elliptic, broad elliptic, ovate and broad ovate where elliptic shape was found in maximum germplasm. The highest quantitative variation was observed in edible leaf weight per plant followed by petiole length and number of lateral branches. Germplasm TRMR-95, NT-34, TRMR-136, NRI-121 had longer leaf and NRI-121, TRMR-136, RC-139, TRMR-12 and NT-33 had broader leaf. Germplasm RC-139 had highest number of lateral branches. The germplasm NQ-68 required maximum days for bolting which was followed by TRMR-136 and RNF-126 that was most important characters for spinach. The dendrogram shown that the maximum thirty-six germplasm were grouped into cluster I and Cluster IV was occupied by only one germplasm. The maximum cluster mean value was observed in Cluster II for the characters leaf length, leaf width and edible leaf weight per plant. The first PC explained 37.10% of the total variability and the second PC explained 22.90% of the variation among fifty-one spinach germplasm. Populations with high scores for the first eigenvectors are leaf length (0.4860), leaf width (0.4827) and petiole length (0.4792) these traits were the most important contributors towards diversity of the germplasm in PC1. Considering
... Plants with stable phenotypes are strong genomic tools and are also a target to identify the alleles by high-throughput sequencing. Advances in sequencing technology have increased genotyping efficiencies, while phenotypic characterization has progressed more slowly over the past decade, restricting the characterization of quantitative characteristics, especially those associated with stress tolerance (White et al. 2012). There are recent developments in phenotyping methods which allow the identification of specific characteristics. ...
... Phenomics technology requires advanced imaging systems, sensors, automations, and computational resources for the phenotyping in plants. These make phenomics a high-throughput approach that is capable of handling thousands of genotypes for the evaluation of hundreds of phenotypic parameters simultaneously (White et al. 2012;Ubbens and Stavness 2017;Tardieu et al. 2017). There are various phenomics platforms available to investigate physiological parameters in plants under different stress conditions, e.g., one such tool is scan analyzer 3D. ...
In recent times, agriculturally important plants face increasing challenges in maintaining productivity, disease control, and welfare of farmers with changing climatic conditions. To accomplish this, the generation and analysis of large volumes of data, especially in the emerging “OMICS” areas of genomics, proteomics, and bioinformatics, is imperative for decision-making over large volumes of data with respect to various crops. Analysis of this large amount of diverged data needs specific tools and techniques. There are various tools and techniques available for the analysis of such data. In this chapter, a detailed discussion on omics data analysis related tools and techniques have been made. This chapter provides a single platform to help the various researchers working in different domains of omics research for analyzing the data.KeywordsGenomicsGenomic selectionGWASOMICSPhenomicsRNAseqQTL
... Plants with stable phenotypes are strong genomic tools and are also a target to identify the alleles by high-throughput sequencing. Advances in sequencing technology have increased genotyping efficiencies, while phenotypic characterization has progressed more slowly over the past decade, restricting the characterization of quantitative characteristics, especially those associated with stress tolerance (White et al. 2012). There are recent developments in phenotyping methods which allow the identification of specific characteristics. ...
... Phenomics technology requires advanced imaging systems, sensors, automations, and computational resources for the phenotyping in plants. These make phenomics a high-throughput approach that is capable of handling thousands of genotypes for the evaluation of hundreds of phenotypic parameters simultaneously (White et al. 2012;Ubbens and Stavness 2017;Tardieu et al. 2017). There are various phenomics platforms available to investigate physiological parameters in plants under different stress conditions, e.g., one such tool is scan analyzer 3D. ...
This chapter addresses the adverse effect of heat stress on plant growth, genes associated with heat stress tolerance and adaptive strategies that can be used to create heat-tolerant plants. CRISPR/Cas9 seems a promising approach regarding stress tolerance. The modified versions of CRISPR/Cas9 like CRISPRi, CRISPRa, base editing and CRISPR multiplexing offers more and more specificity and advanced editing options and minimizes the off-target effect. The versatility of CRISPR/Cas9 brings a new revolution in the field of plant science to alleviate abiotic stress like heat stress.KeywordsHeat stressCRISPR/Cas 9CRISPRiCRISPRaBase editingCRISPR multiplexing
... Compared to traditional plant sampling methods, spectral measurements are more rapid and less destructive (Sahoo et al., 2015) and offer several advantages for increasing the frequency and spatial coverage of crop growth and health estimates (Duranovich et al., 2020). However, practical considerations for proximal sensor deployment remain a limiting factor on the amount and quality of spectral data that can be collected, particularly under adverse environmental conditions (White et al., 2012). For example, high air temperatures have caused field spectroradiometers to overheat and shutdown (Huang et al., 2016;Hankerson, 2018), because extreme heat can overwhelm their onboard thermal electric cooling systems. ...
Background
Advancements in field spectrometry have the potential to increase understanding of crop growth and development in response to hot and dry environments. However, as with any instrument used for scientific advancement, it is important to continue developing and optimizing data collection protocols to promote efficiency, safety, and data quality. The goal of this study was to develop a novel data collection method, involving a proximal sensing cart with onboard cooling equipment, to improve deployments of a field spectroradiometer in a hot and dry environment. Advantages and disadvantages of the new method were compared with the traditional backpack approach and other approaches reported in literature.
Results
The novel method prevented the spectroradiometer from overheating and nearly eliminated the need to halt data collection for battery changes. It also enabled data collection from a significantly larger field area and from more field plots as compared to the traditional backpack method. Use of a custom cooling box to stabilize operating temperatures for the field spectroradiometer also improved stability of white panel data both within and among collections despite outside air temperatures in excess of 30°C.
Conclusions
As compared to traditional data collection approaches for measuring spectral reflectance of field crops in a hot and dry environment, use of a proximal sensing cart with a customized equipment cooling box improved spectroradiometer performance, increased practicality of equipment transport, and reduced operator safety concerns.
... In particular, the use of cameras in the context of plant phenotyping has been explored, and substantial progress has been made in feature extraction and subsequent data analysis ( [10][11][12][13]). Modern plant phenotyping methods were first established under controlled conditions in climate chambers [14][15][16] and later also in the field [17][18][19]. A range of methods and sensors has evolved, and image-based high-throughput field phenotyping (HTFP) is widely used in research, especially in wheat [20,21]. ...
Abiotic stresses such as heat and frost limit plant growth and productivity. Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence. Winter crops show senescence caused by cold spells, visible as declines in leaf area. We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field. Thirty-six winter wheat genotypes were measured in multiple years. A concept termed “frost damage index” (FDI) was developed that, in analogy to growing degree days, summarizes frost events in a cumulative way. The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness. The FDI concept could be adapted to other factors such as drought or heat stress. While commonly not considered in plant growth modeling, integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.
... Moreover, the methods for characterizing genomes and phenomes have not advanced at the same rate (Moore et al., 2013;Yang et al., 2020). While genomic techniques have benefited from new developments in DNA sequencing, including higher resolution and lower costs, plant phenotyping has improved at a slower rate and commercial plant phenotyping platforms remained costly and practically inaccessible for purchase by the majority of plant laboratories (Jackson et al., 2011;Reynolds et al., 2019;Shendure & Ji, 2008;White et al., 2012). More importantly, this gap between genomic and phenomic technologies are preventing the full use of resources available in several plant species such as mutant collections or diversity panels to pursue genome-wide studies or the characterization of complex traits at a higher resolution (Xiao et al., 2017). ...
The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome ). High‐throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf , an open‐source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf , coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf‐specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high‐throughput screens to identify and characterize previously unidentified phenotypes in a leaf‐specific time‐dependent manner. Moreover, the modular and open‐source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.
... These signatures are used to calculate spectral indices, providing information on the light absorption properties of plants at specific wavelengths [19,20]. Recent advancements in remote sensing technologies and data processing have made it possible to apply these techniques in both field and controlled growing conditions [21,8], offering a rapid and nondestructive approach to plant screening [22]. Integrating high-throughput phenotyping through remote sensing tools, along with the ability to account for environmental factors, will significantly improve selection efficiency in plant breeding [10]. ...
Accelerating crop improvement with enhanced adaptability to changing climatic conditions and meeting the ever-increasing global food demand requires urgent action. To achieve this, we must employ advanced molecular breeding techniques, such as marker-assisted selection, marker-assisted backcrossing, genomic selection, genome editing, and targeted mutation. However, these Review Article Anand et al.; Int. 122 approaches demand the screening of large populations to identify potential genes and genotypes. Unfortunately, a significant bottleneck lies in the absence of high-throughput plant phenotyping methods that can rapidly and cost-effectively facilitate data-driven genotype selection in plant breeding. Traditional phenotyping methods, reliant on trained experts, are slow, expensive, labour-intensive, subjective, and often require destructive sampling. Proximal remote sensing technologies, including RGB imaging, thermal imaging, hyperspectral imaging, multispectral imaging, and fluorescence imaging, offer a non-destructive and rapid collection of detailed phenotypic data, providing valuable insights into various plant traits at different growth stages. High-throughput phenotyping platforms, such as Conveyor-Type Indoor, Benchtop-Type Indoor, Unmanned Aerial Platform (UAP), and Manned Aerial Platform (MAP), utilize a combination of the aforementioned remote sensing technologies. This review article aims to explore the integration of proximal remote sensing and molecular breeding approaches, showcasing how this synergistic approach can expedite crop improvement efforts. By emphasizing the benefits, challenges, and future prospects of this integrative approach, we hope to pave the way for sustainable and productive agriculture, ensuring food security in the face of changing environmental conditions.
... [14]. Crop genetic improvement has been hampered by the lack of field-based high-throughput phenotyping methods [15,16], but recent reviews [17,18,19] have highlighted the opportunities now provided by sensor technology and the digital age. Highthroughput phenotyping platforms have been created to automate data collection on numerous plants to facilitate the gathering of phenotypic measurements [20,21,22]. ...
The global challenge of feeding the world demands attention due to the projected population increase to 10.9 billion by 2050. Abiotic and biotic stressors, such as heat, drought, diseases, and pests, further compound the difficulties faced in achieving sufficient agricultural output. Early detection of crop stress is vital to mitigate yield loss and find appropriate agrotechnical solutions. However, the complex interactions between abiotic and biotic stressors and their impact on plant growth and yield present challenges in plant phenotyping and breeding. This review discusses recent advances in remote sensing technologies which offer promising solutions to overcome these challenges. Low-cost, reliable sensors and technologies facilitate data collection and interpretation, paving the way for proximal sensing and high-throughput phenotyping platforms. These automated platforms, equipped with imaging devices, enable non-destructive data collection and monitoring of plant properties over time. Optical methods like hyperspectral sensors, RGB imaging, remote sensing, and chlorophyll fluorescence contribute to the early identification of plant stress causes, facilitating the development of control strategies. By providing accurate and timely information on crop stress, these technologies offer essential support in enhancing agricultural productivity and ensuring food security for a growing global population.
... Furthermore, field measurements serve as a significant test for the relevance of the laboratory and greenhouse approaches. For FBP, airplane and satellite-based systems are used at field scale, but studies using proximal (close-range) sensing are typically the only method that can provide reliable data with sufficient resolution, multiple angles, and illumination control, as well as at a closer proximity to the target to the sensors (White et al., 2012) [60] . ...
Biotic and abiotic stress are the major constrains resulting in crop yield reduction and economic losses. It is estimated that the human population will reach to 9 billion by 2050, and current food production must be doubled to meet the needs of the growing population. Therefore, it is the need of hour to increase crop productivity. Advancement in high-throughput phenotyping technologies has progressed significantly in the last decade. These technologies offer precise measurements of desired traits and strategies to screen large population of plants for a particular phenotype under diversified environments employing advanced robotics, high-tech sensors, imaging systems and computing power to unravel the genetic basis of complex traits associated with plant growth and development. Advanced bioinformatics tools further facilitate the analysis of large-scale multi-dimensional, high-resolution data collected through phenotyping from the gene to the whole-plant level under a specific environment and management practices. With the help of integrated approach of genotyping and phenotyping, gene functions and environmental responses can be understood as well. Moreover, it will also help in finding more relevant solutions for the major problem that tend to limit crop production. This review focuses on the recent advances in plant phenomics, various imaging techniques, highlights different field and confined high-throughput technologies for utilization in forward and reverse genetics.
... Key physiological traits, such as water-use efficiency, can be measured through carbon isotope discrimination using leaf sampling. Other essential parameters, including photosynthesis, chlorophyll content, thermal imaging of the canopy, transpiration, s tomatal conductance, root depth, and mass, directly or indirectly reflect plant water s tatus and functional ability under s tress conditions ( Figure 6) (White et al., 2012). For traits that involve a combination of multiple factors, like canopy cooling, can be influenced by high root density, s tomatal conductance, and hormonal regulation, fieldbased evaluation becomes more pertinent. ...
Biotic and abiotic stress factors significantly impede crop productivity and lead to substantial economic losses. Given the projected human population of 9 billion by 2050 and the necessity to double current food production to meet the demands
of this growing populace, enhancing crop productivity has become a pressing concern. In recent years, substantial progress has been made in the field of high-throughput phenotyping technologies, enabling precise measurements of desired traits and efficient screening of large plant populations under diverse environmental conditions. These advancements involve the integration of advanced robotics, high-tech sensors, imaging systems, and computing power to unravel the genetic basis of complex traits associated with plant growth and development. Furthermore, advanced bioinformatics tools have emerged to analyze the vast amounts of multi-dimensional, high-resolution data collected through phenotyping at both the genetic and whole-plant levels, considering specific environmental conditions and
management practices. The integration of genotyping and phenotyping approaches facilitates a comprehensive understanding of gene functions and their responses to various environmental stimuli. This integrated approach holds significant promise for identifying solutions to the major constraints limiting crop production. This review focuses on the recent breakthroughs in plant phenomics and various imaging techniques, emphasizing the applications of different high-throughput technologies in both controlled and natural field conditions. These advancements are crucial steps towards addressing the challenges posed by stress factors and ultimately achieving sustainable and increased crop yields to meet the demands of the growing global population.
... These robots operate autonomously, eliminating the need for experienced operators to carry out various farming tasks [3]. This autonomous capability represents a major advantage over traditional tractor-based systems [4,5]. Agricultural robots play a vital role in assisting farmers to achieve higher output yields through various means. ...
This paper presents a detailed design of a skid-steering mobile platform with four wheels, along with a Cartesian serial (PPP) manipulator. The aim of this design is to enable the platform to perform various tasks in the agricultural process. The parallel manipulator designed can handle heavy materials in the agricultural field. An experimental robotic harvesting scenario was conducted using parallel manipulator-based end-effectors to handle heavy fruits such as watermelon or muskmelon. The conceptual and component design of the different models was carried out using the Solidworks modeling package. Design specifications and parametric values were utilized during the manufacturing stage. The mobile manipulator was simulated on undulating terrain profiles using ADAMS software. The simulation was analyzed for a duration of 15 s, and graphs depicting the distance, velocity, and acceleration were evaluated over time. Proportional derivative control and proportional derivative-like conventional sliding surface control were applied to the model, and the results were analyzed to assess the error in relation to the input and desired variables. Additionally, a structural analysis was performed to ensure minimal deformation and the highest safety factor for the wheel shaft and L bracket thickness. Throughout the fabrication and prototype development, calibration tests were conducted at various X-, Y-, and Z-axis frame mounting stages. The objective was to minimize the lateral and longitudinal deviation between the parallel linear motion (LM) rails. Once the fabrication and prototype construction was completed, field testing was carried out. All mechanical movements in the lateral and longitudinal directions functioned according to the desired commands given by the Arduino Mega, controlled via a six-channel radio frequency (RF) controller. In the context of agriculture, the grippers utilizing parallel mechanisms were also subjected to testing, demonstrating their ability to handle sizable cylindrical and spherical fruits or vegetables, as well as other relevant objects.
... As a result, the phenotype offers the strongest link between surroundings and plant genetics. While phenotypic characterisation has advanced more slowly over the past ten years due to improvements in sequencing technologies, this has limited the discovery of quantitative characteristics, especially those linked to stress resistance [293]. ...
Flax, or linseed, is considered a “superfood”, which means that it is a food with diverse
health benefits and potentially useful bioactive ingredients. It is a multi-purpose crop that is prized
for its seed oil, fibre, nutraceutical, and probiotic qualities. It is suited to various habitats and
agro-ecological conditions. Numerous abiotic and biotic stressors that can either have a direct or
indirect impact on plant health are experienced by flax plants as a result of changing environmental
circumstances. Research on the impact of various stresses and their possible ameliorators is prompted
by such expectations. By inducing the loss of specific alleles and using a limited number of selected
varieties, modern breeding techniques have decreased the overall genetic variability required for
climate-smart agriculture. However, gene banks have well-managed collectionns of landraces, wild
linseed accessions, and auxiliary Linum species that serve as an important source of novel alleles. In
the past, flax-breeding techniques were prioritised, preserving high yield with other essential traits.
Applications of molecular markers in modern breeding have made it easy to identify quantitative
trait loci (QTLs) for various agronomic characteristics. The genetic diversity of linseed species and
the evaluation of their tolerance to abiotic stresses, including drought, salinity, heavy metal tolerance,
and temperature, as well as resistance to biotic stress factors, viz., rust, wilt, powdery mildew, and
alternaria blight, despite addressing various morphotypes and the value of linseed as a supplement,
are the primary topics of this review.
... As a result, the phenotype offers the strongest link between surroundings and plant genetics. While phenotypic characterisation has advanced more slowly over the past ten years due to improvements in sequencing technologies, this has limited the discovery of quantitative characteristics, especially those linked to stress resistance [293]. ...
Flax, or linseed, is considered a “superfood”, which means that it is a food with diverse health benefits and potentially useful bioactive ingredients. It is a multi-purpose crop that is prized for its seed oil, fibre, nutraceutical, and probiotic qualities. It is suited to various habitats and agro-ecological conditions. Numerous abiotic and biotic stressors that can either have a direct or
indirect impact on plant health are experienced by flax plants as a result of changing environmental circumstances. Research on the impact of various stresses and their possible ameliorators is prompted by such expectations. By inducing the loss of specific alleles and using a limited number of selected varieties, modern breeding techniques have decreased the overall genetic variability required for
climate-smart agriculture. However, gene banks have well-managed collections of landraces, wild linseed accessions, and auxiliary Linum species that serve as an important source of novel alleles. In the past, flax-breeding techniques were prioritised, preserving high yield with other essential traits. Applications of molecular markers in modern breeding have made it easy to identify quantitative trait loci (QTLs) for various agronomic characteristics. The genetic diversity of linseed species and the evaluation of their tolerance to abiotic stresses, including drought, salinity, heavy metal tolerance, and temperature, as well as resistance to biotic stress factors, viz., rust, wilt, powdery mildew, AND Alternaria blight, despite addressing various morphotypes and the value of linseed as a supplement, are the primary topics of this review.
... Genomic selection can be combined with high-throughput phenotyping methods to obtain accurate phenotypic information for traits of interest (Cobb et al., 2013;Watanabe et al., 2017;White et al., 2012). Jia and Jannink (2012) reported that high-throughput phenotyping could predict complex traits such as grain yield in multitrait GS modelling. ...
Maize is a commodity crop providing millions of people with food, feed, industrial raw material and economic opportunities. However, maize yields in Africa are relatively stagnant and low, at a mean of 1.7 t ha−1 compared with the global average of 6 t ha−1. The yield gap can be narrowed with accelerated and precision breeding strategies that are required to develop and deploy high-yielding and climate-resilient maize varieties. Genomic and phenotypic selections are complementary methods that offer opportunities for the speedy choice of contrasting parents and derived progenies for hybrid breeding and commercialization. Genomic selection (GS) will shorten the crop breeding cycle by identifying and tracking desirable genotypes and aid the timeous commercialization of market-preferred varieties. The technology, however, has not yet been fully embraced by most public and private breeding programmes, notably in Africa. This review aims to present the importance, current status, challenges and opportunities of GS to accelerate genetic gains for economic traits to speed up the breeding of high-yielding maize varieties. The first section summarizes genomic selection and the contemporary phenotypic selection and phenotyping platforms as a foundation for GS and trait integration in maize. This is followed by highlights on the reported genetic gains and progress through phenotypic selection and GS for grain yield and yield components. Training population development, genetic design and statistical models used in GS in maize breeding are discussed. Lastly, the review summarizes the challenges of GS, including prediction accuracy, and integrates GS with speed breeding, doubled haploid breeding and genome editing technologies to increase breeding efficiency and accelerate cultivar release. The paper will guide breeders in selection and trait introgression using GS to develop cultivars preferred by the marketplace.
... However, UAV imagery provides elasticity in operations; users can perform multitemporal and custom-defined campaigns. Low-altitude UAV flights are easy and safer to conduct, provide a very high spatial resolution of centimeters level and avoid the majority of atmospheric interferences and clouds [Han et al., 2021;White et al., 2012;Zarco-Tejada et al., 2013]. UAVs have widespread implementation and are suitable for small farms but have weaknesses like limited payload capacity, short flight duration, technical complexity, and little spatial coverage. ...
Spatial information on plant-water requirement is the most crucial input for designing an efficient site-specific irrigation
system. In quantifying this spatial information, canopy temperature-derived crop water stress maps could provide a
potential solution. With the support of modern, advanced, and cost-effective remote sensing platforms like Unmanned
Aerial Vehicle (UAV), aircraft, and Satellite, remote sensing data can be systematically collected with varying degrees of
efficiency for spatial canopy temperature assessment. However, each platform provides remote sensing data at varying
degrees of spectral and spatial resolutions, which can impact the user’s ability to develop canopy temperature-based spatial
water stress maps and implement precision irrigation systems. Therefore, the main goals of this study were 1) to assess the
feasibility and accuracy of UAV, aircraft, and satellite-based imaging for crop canopy temperature and health mapping;
and 2) to compare and contrast the resolution of water-stressed regions identification for precision irrigation technology
implementation. Thermal infrared (TIR) and multispectral images were obtained over a four-acre cornfield using a
quadcopter (Matrice-100), aircraft (Ceres Imaging), and Satellite (Landsat-8). Spatial maps of canopy temperature and
NDVI were developed using these images and analyzed for capacity to capture water requirements and crop health
accurately. UAV imagery outperformed the other two platforms in providing detailed imagery and sensing changes in crop
health throughout the field. For a sample area of dimension 82 m x 44 m, the UAV imagery provided 683 different types
of canopy temperature values. In contrast, aircraft imagery provided 158 different values, followed by satellite imagery
which provided only 5-6 variations in canopy temperature to represent the same area. Moderate and low spatial resolution
imagery from aircraft (0.9-1.2 m/pixel) and satellite (30 m/pixel) was limited in detecting inter-row variability and
outputting the average pixels of the crop canopy and inter-row space. Whereas high-resolution UAV imagery (1.5 cm/pixel
– 6 cm/pixel) precisely distinguished inter-row gap from plants and provided crop-only pixels without mixing with
background soil. UAV imagery was precise and sensitive in detecting crop variability between two nozzles of an irrigation
pivot, while aircraft imagery was less precise and sensitive. Satellite imagery was not able to capture the variations at this
small scale. So, overall, UAV and aircraft imagery remains competitive in providing infield crop health variability for site-
specific management in agriculture. Satellite imagery is limited in providing infield crop health variability to design site-
specific irrigation, especially for small-scale farms.
... Complex traits are controlled by different genes with minor and major effects and environmental conditions (Fig. 3). Recent studies showed the successful use of HTP in selecting superior genotypes in different breeding programs (Cobb et al., 2013;White et al., 2012). HTP increases the rate of genetic gain and provides opportunities to maximize trait heritabilities in stressful environments to increase the efficiency of plant breeding programs (Cabrera-Bosquet et al., 2012;Reynolds et al., 2020). ...
Global agriculture production needs to be well-positioned to feed the fast-growing world population. Plant breeders increase overall plant performance to meet the global food demand, evaluate large genetic populations of breeding lines rapidly and accurately to identify variation underlying important traits; increasingly better adaption to changing environments. However, accurate, quick, and non-destructive characterization of all lines simultaneously, remains challenging. Therefore, there is a dire need to strengthen plant breeding not only for breeding techniques but also in other areas, such as improved phenotyping. Advanced remote and proximal sensing have increased the pace to rapidly overcome phenotyping shortcomings and address relevant biological questions related to plant breeding, field scouting, and crop management. However, it is still unclear how far and to what extent remote and proximal sensing has come to help plant breeding in the past two decades. Here we try to address this question by reviewing varied aspects of remote and proximal sensing applications in plant breeding and identify possible solutions to the existing shortcomings provided as the future direction for plant breeding programs.
... The proportions of the canopy are an essential component of the structure of the canopy and is essential for production of plant biomass, fruit, and photosynthesis. However, evaluation of canopy size becomes a constraint that restricts cotton breeding and hereditary studies, particularly for large crop inhabitants and high-dimensional characters (White et al., 2012;Cobb et al., 2013). Cotton (and other) breeding programs and genetics studies might benefit from accurate high throughput methodologies for assessing canopy size (Reynolds & Langridge, 2016). ...
AB S T RA C T The architecture of the canopy tends to affect how light is reflected and distributed within it. Rational modelling and trimming can improve crop architecture, maximize the use of space, light, and resources such as land, and lay the groundwork for initial maturing, and maximum yield. Determining the interception of light inside the canopy is critical aimed at increasing the population's photosynthetic production. By implementing cultural practices that produce optimal plant populations and alter the plant canopy components, it is possible to maximize light utilization in the production of cotton. In order to forecast the expected yield for uses like crop management and agronomic decision-making, as well as to investigate potential impacts of environmental alteration on food security, crop growth models are used to estimate the correlation between plants and the environment. In this study, we highlight the light interception, canopy architecture and their use in crop growth models to improve crop productivity. Constructing a strong technological system capable of phenotyping crops in a high-throughput, multidimensional, large-data, efficient, and mechanically determining manner is the ultimate objective.
... Whereas manual, i.e. 'low-throughput', phenotyping methods dominate ecological field studies, in agricultural research and applications 'high-throughput phenotyping' became standard (Dhondt et al., 2013;Rosenqvist et al., 2019). A variety of approaches are available to acquire phenotypic raw data such as cameras for RGB color recording, multispectral units for reflectance measuring in the visible, infrared, or near-infrared spectrum, lasers that measure distance for 3D imaging and thermal sensors, among others (White et al., 2012;Barbedo, 2019). The application of such devices is often limited because either individual plants need to be transported to a stationary scanner (plant-to-sensor) or scanners are installed in greenhouses or outdoor facilities where they move over a defined set of plants (sensor-to-plant) (Busemeyer et al., 2013;Fiorani and Schurr, 2013;Demidchik et al., 2020). ...
Plant traits are informative for ecosystem functions and processes and help to derive general rules and predictions about responses to environmental gradients, global change and perturbations. Ecological field studies often use 'low-throughput' methods to assess plant phenotypes and integrate species-specific traits to community-wide indices. In contrast, agricultural greenhouse or lab-based studies often employ 'high-throughput phenotyping' to assess plant individuals tracking their growth or fertilizer and water demand. In ecological field studies, remote sensing makes use of freely movable devices like satellites or unmanned aerial vehicles (UAVs) which provide large-scale spatial and temporal data. Adopting such methods for community ecology on a smaller scale may provide novel insights on the phenotypic properties of plant communities and fill the gap between traditional field measurements and airborne remote sensing. However, the trade-off between spatial resolution, temporal resolution and scope of the respective study requires highly specific setups so that the measurements fit the scientific question. We introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies that provides complementary multi-faceted data of plant communities. We customized an automated plant phenotyping system for its mobile application in the field for 'digital whole-community phenotyping' (DWCP), capturing the 3-dimensional structure and multispectral information of plant communities. We demonstrated the potential of DWCP by recording plant community responses to experimental land-use treatments over two years. DWCP captured changes in morphological and physiological community properties in response to mowing and fertilizer treatments and thus reliably informed about changes in land-use. In contrast, manually measured community-weighted mean traits and species composition remained largely unaffected and were not informative about these treatments. DWCP proved to be an efficient method for characterizing plant communities, complements other methods in trait-based ecology, provides indicators of ecosystem states, and may help to forecast tipping points in plant communities often associated with irreversible changes in ecosystems.
... Therefore, the plot-average value of the spectral indices was used as input in the NNIOA to calculate the N recommendation rates for each field. Previous studies mainly collected the spectral data using the proximal sensors such as Greenseeker, RapidSCAN and Cropcircle in small-sized fields, or based on a tractor-mounted spectrometer in medium-sized fields (White et al., 2012). However, these spectral systems may not be suitable for data collected at the larger area due to the limit of low measurement efficiency. ...
Remote sensing has been used for assisting the precision nitrogen (N) management in wheat (Triticum aestivum) production. This study aimed to develop an efficient and energy saving N management strategy based on multi-source data for winter wheat at the farm scale. Five field experiments involving different cultivars and N treatments were conducted to establish and validate the N management strategy in 2017–2021. UAV multi-spectral images, plant sampling, weather and field management data collection were carried out synchronously at Feekes 6.0. Four machine learning methods were used to integrate multi-variate information to determine the optimal parameters in N regulation algorithm. The results showed random forest (RF) algorithm performed best for plant dry matter (R² = 0.78) and plant N accumulation (R² = 0.83) estimation, a N nutrition index optimized algorithm (NNIOA), driven by multi-source data, was developed and used for guiding in-season N application. The NNIOA efficiently regulated the deficient, optimal and excessive N status through up- (54.17%), fine- (0.67%) and downward- (18.18%) adjustment of N fertilizers, respectively, while the optimal N treatment achieved highest net profit, energy use efficiency (EUE) and energy productivity (EP). Compared with farmer’s practices, the NNIOA increased partial factor productivity (PFP), net profit, EUE and EP by 19.60–27.94%, 22.47–45.13 $ ha⁻¹, 6.94–13.07% and 8.36–12.29%, respectively, while reduced N input (16.77–21.67%), energy input (8.13–10.74%) and CO2 emission (7.60–10.11%) without any yield reduction at study farms. In conclusion, this study supplied a precision N management strategy to implement variable N application for sustainable wheat production at farm scale.
... Plant growth traits ( Table 2) have been estimated more often (especially for breeding purposes) using sUASbased imagery compared to other traits reported in the literature because these traits are correlated with crop development and yield [118]. For example, plant height has often been reported as positively correlated with fiber yield and water-use efficiency under low soil moisture conditions in cotton [119,120]. Although sUAS-based plant height phenotyping was reported for blueberry in 2017 [121], this trait has mostly been estimated in row crops such as cotton, soybean, maize, and sorghum [52,54,69,85,[122][123][124]. ...
High-throughput plant phenotyping (HTPP) involves the application of modern information technologies to evaluate the effects of genetics, environment, and management on the expression of plant traits in plant breeding programs. In recent years, HTPP has been advanced via sensors mounted on terrestrial vehicles and small unoccupied aircraft systems (sUAS) to estimate plant phenotypes in several crops. Previous reviews have summarized these recent advances, but the accuracy of estimation across traits, platforms, crops, and sensors has not been fully established. Therefore, the objectives of this review were to (1) identify the advantages and limitations of terrestrial and sUAS platforms for HTPP, (2) summarize the different imaging techniques and image processing methods used for HTPP, (3) describe individual plant traits that have been quantified using sUAS, (4) summarize the different imaging techniques and image processing methods used for HTPP, and (5) compare the accuracy of estimation among traits, platforms, crops, and sensors. A literature survey was conducted using the Web of ScienceTM Core Collection Database (THOMSON REUTERSTM) to retrieve articles focused on HTPP research. A total of 205 articles were obtained and reviewed using the Google search engine. Based on the information gathered from the literature, in terms of flexibility and ease of operation, sUAS technology is a more practical and cost-effective solution for rapid HTPP at field scale level (>2 ha) compared to terrestrial platforms. Of all the various plant traits or phenotypes, plant growth traits (height, LAI, canopy cover, etc.) were studied most often, while RGB and multispectral sensors were most often deployed aboard sUAS in HTPP research. Sensor performance for estimating crop traits tended to vary according to the chosen platform and crop trait of interest. Regardless of sensor type, the prediction accuracies for crop trait extraction (across multiple crops) were similar for both sUAS and terrestrial platforms; however, yield prediction from sUAS platforms was more accurate compared to terrestrial phenotyping platforms. This review presents a useful guide for researchers in the HTPP community on appropriately matching their traits of interest with the most suitable sensor and platform.
... Phenotyping, therefore, remains a bottleneck in the plant breeding pipeline for future breeding advances. Several high-throughput phenotyping tools have been investigated for plant growth and architecture phenotyping, including aerial-(i.e., drones, helicopters, aerostats) and ground-driven (i.e., tractors, vehicles, manual cameras) imaging-based remote sensing techniques (Furbank and Tester, 2011;White et al., 2012;Araus and Cairns, 2014;Rebetzke et al., 2019). For fruit quality phenotyping which involves physical and chemical evaluation of fruits, the suitability of spectroscopy has been investigated as a non-invasive technique for various fruit and vegetable commodities: Cheng et al. (2004) for cucumber; Sánchez et al. (2012) for strawberry; Sun et al. (2012) for orange; Pissard et al. (2013) for apple; Maniwara et al. (2019) for purple passion fruit; Donis-González et al. (2020) for grape and peach; and Kaur et al. (2020) for tomato and pepper. ...
Fruit quality phenotyping is a bottleneck in plant breeding. The present work aimed to investigate the applicability of visible (Vis) and near-infrared (NIR) spectroscopy for quality evaluation in dry red chili powder. We constructed prediction models for the American Spice Trade Association (ASTA)-colour and the Scoville Heat Unit (SHU)-pungency pepper traits using spectroscopy and multivariate statistical techniques. Predictive partial least squares (PLS) models were successfully achieved with high correlations (r) between the predicted and reference values for calibration and validation (r = 0.955 and 0.928 for ASTA-colour; r = 0.941 and 0.918 for SHU-pungency). Spectroscopy data from visible and short-wave radiation (Vis-SWNIR) provided the most robust (residual predictive deviation value) model for ASTA-colour (RPD = 2.84) and long-wave radiation (LWNIR) for SHU-pungency (RPD = 2.48). Spectral categories for wavelength range selection, variable importance for effective wavelength selection, and root mean press-statistic for factor selection were important criteria for PLS. Trait variance and distribution were also important criteria for the predictive capacity and power of the models. In conclusion, non-invasive spectroscopy was a promising tool in our study for dry red chili quality phenotyping.
... While other indices might be applicable, NDVI was chosen because it is the VI with the most widespread usage. NDVI is a good biomass estimator and reveals aggregating numbers of the size and density of the canopy [33]. NDRE is a similar measure that is used to overcome problems with saturation at late phenological stages; this is one of the shortcomings of NDVI. ...
In recent years, there has been a growing need for accessible High-Throughput Plant Phenotyping (HTPP) platforms that can take measurements of plant traits in open fields. This paper presents a phenotyping system designed to address this issue by combining ultrasonic and multispectral sensing of the crop canopy with other diverse measurements under varying environmental conditions. The system demonstrates a throughput increase by a factor of 50 when compared to a manual setup, allowing for efficient mapping of crop status across a field with crops grown in rows of any spacing. Tests presented in this paper illustrate the type of experimentation that can be performed with the platform, emphasizing the output from each sensor. The system integration, versatility, and ergonomics are the most significant contributions. The presented system can be used for studying plant responses to different treatments and/or stresses under diverse farming practices in virtually any field environment. It was shown that crop height and several vegetation indices, most of them common indicators of plant physiological status, can be easily paired with corresponding environmental conditions to facilitate data analysis at the fine spatial scale.
... Phenotyping techniques in plant breeding are promising and contribute significantly to the characterization, selection and plant breeding programs, as it is non-destructive techniques and promote faster results (White et al., 2012) and could be adapted to selected plants to be use in green roofs. These innovative image analysis techniques help to understand plant responses at the leaf, plant and canopy level (Potgieter et al., 2018). ...
The objective was to evaluate the use of thermal and optical properties measurements in the characterization of the genus Paspalum for use on green roofs in a tropical environment. The experiment was conducted at the Universidade Federal Rural de Pernambuco – UFRPE from May to June 2019. The number of sixteen Paspalum accessions with prostrate and upright growth habits was evaluated. In a simulated modular green roof were evaluated: Soil-Adjusted Vegetation Index (SAVI); leaf (L) and canopy (C) temperature by thermometry (TM); and by thermography (TG). The determination coefficient (R2) between SAVI and the coverage area are of 0.84, with green matter (R2 = 0.73) and dry matter (R2 = 0.56). No significant difference was observed between the accessions for leaf temperature measure by TM and TG, and for canopy TM. Nevertheless, significant difference to canopy TG were observed, with lower canopy temperatures in accessions with upright growth habit than those with prostrate habit. The relationship between the techniques showed high values of determination coefficient to TML with TGL (R2 = 0.85) and TMC with TGC (R2 = 0.91). Given the above, the SAVI can be used to estimate the coverage area capacity (CC). Leaf and canopy temperatures can be used as an indication of water deficit in Paspalum accessions, using both the infrared thermometer (more accessible) and the thermographic camera. Canopy thermography can also be used to evaluate the thermal performance of green roofs. Thermographic images indicated that Paspalum accessions of upright growth habits may provide better thermal comfort.
... One way around this is to use high throughput field-based phenotyping, which requires criterion-based experimental approaches, equipment, and data analysis tools to gain access to useful traits and adaptations that are important for stress-recovery. Additional innovations are needed to guarantee that the improvements will be effective and reliable (White et al., 2012). In light of the increased metal tolerance, phenomics has the potential to become one of the most important strategies for improving biological systems (Khalid et al., 2019). ...
Soil and water pollution, rapid industrialization, contaminated irrigation-water, increased waste-production and surge in agricultural land leads to the accumulation of Heavy Metals (HM) with time. HM contamination has raised concern over the past years and new remediation strategies are required to deal with it. HM-contaminated soil is often used for the production of food, which makes a gateway for toxic metals into the food-chain, thereby affecting food security and human health. To avoid HM-toxicity, decontamination of important resources is essential. Therefore, exploring phytoremediation for the removal, decomposition and detoxification of hazardous metals from HM-contaminated sites is of great significance. Hyper-accumulator plants can efficiently remove HMs. However, despite many hyper-accumulator plant species, there is a research gap in the studies of phytotechnology. Hence biotechnological efforts advocating omics studies i.e. genomics, transcriptomics, proteomics, metabolomics and phenomics are in order, the purpose being to select and enhance a plant's potential for the process of phytoremediation to be more effective. There is a need to study newly developed high-efficiency hyper-accumulator plants as HM-decontaminator candidates for phytoremediation and phytomining. Therefore, this review focuses on various strategies and bio-technological methods for the removal of HM contaminants from sites, with emphasis on the advancement of phytoremediation, along with applications in cleaning up various toxic pollutants.
... High-throughput methods can be used to characterize large numbers of genotypes accurately. Most high-throughput phenotyping methods are based on robotics, image analysis, and remote sensing technologies (Walter et al., 2012;White et al., 2012;Araus and Cairns, 2014). Remote sensing techniques are non-destructive, rapid, and large-scale integrated (i.e. at canopy level) to assess the crop performance. ...
... Assessing plant characteristics manually, which is still commonly used in practical applications of phenology, is laborintensive, time-consuming, and prone to human error due to fatigue and distraction during data collection [32,33]. This becomes a bottleneck limiting breeding programs and genetic studies, especially for large plant populations and high-dimensional traits (e.g., canopy volume) [1,[34][35][36][37]. Therefore, plant breeders and agronomists have identified the need for a high-throughput phenotyping (HTP) system that can measure phenotypic traits such as plant height, volume, canopy cover, and vegetation indices (VIs) with reasonable accuracy [38]. ...
Modeling cotton plant growth is an important aspect of improving cotton yields and fiber quality and optimizing land management strategies. High-throughput phenotyping (HTP) systems, including those using high-resolution imagery from unmanned aerial systems (UAS) combined with sensor technologies, can accurately measure and characterize phenotypic traits such as plant height, canopy cover, and vegetation indices. However, manual assessment of plant characteristics is still widely used in practice. It is time-consuming, labor-intensive, and prone to human error. In this study, we investigated the use of a data-processing pipeline to estimate cotton plant height using UAS-derived visible-spectrum vegetation indices and photogrammetric products. Experiments were conducted at an experimental cotton field in Aliartos, Greece, using a DJI Phantom 4 UAS in five different stages of the 2022 summer cultivation season. Ground Control Points (GCPs) were marked in the field and used for georeferencing and model optimization. The imagery was used to generate dense point clouds, which were then used to create Digital Surface Models (DSMs), while specific Digital Elevation Models (DEMs) were interpolated from RTK GPS measurements. Three (3) vegetation indices were calculated using visible spectrum reflectance data from the generated orthomosaic maps, and ground coverage from the cotton canopy was also calculated by using binary masks. Finally, the correlations between the indices and crop height were examined. The results showed that vegetation indices, especially Green Chromatic Coordinate (GCC) and Normalized Excessive Green (NExG) indices, had high correlations with cotton height in the earlier growth stages and exceeded 0.70, while vegetation cover showed a more consistent trend throughout the season and exceeded 0.90 at the beginning of the season.
... Traditional, manual, phenotype information collection methods are often destructive, inefficient, and labor-intensive, and it can be hard to obtain growth information [20,21]. For example, for trees with tall individuals, long growth spans, and complex genome sequences and habitats, large-scale phenotypic trait monitoring is difficult and expensive, which affects the rapid development of the forest breeding industry [22,23]. Unmanned aircraft remote sensing technology can gather high-resolution, multi-spectral images at a low altitude, and it can not only obtain extensive forest condition data in a timely and nondestructive manner, but it can also improve the fineness to the structure of the branches and leaves, and the reflective spectrum within an individual tree canopy, thereby increasing the accuracy of the information extraction and the efficiency of the data acquisition [24,25]. ...
The quantitative, accurate and efficient acquisition of tree phenotypes is the basis for forest “gene-phenotype-environment” studies. It also offers significant support for clarifying the genetic control mechanisms of tree traits. The application of unmanned aerial vehicle (UAV) remote sensing technology to the collection of phenotypic traits at an individual tree level quantitatively analyses tree phenology and directionally evaluates tree growth, as well as accelerating the process of forest genetics and breeding. In this study, with the help of high-resolution, high-overlap, multispectral images obtained by an UAV, combined with digital elevation models (DEMs) extracted from point clouds acquired by a backpack LiDAR, a high-throughput tree structure and spectral phenotypic traits extraction and a genetic selection were conducted in a trial of Eucalyptus clones in the State-owned Dongmen Forest Farm in the Guangxi Zhuang Autonomous Region. Firstly, we validated the accuracy of extracting the phenotypic parameters of individual tree growth based on aerial stereo photogrammetry point clouds. Secondly, on this basis, the repeatability of the tree growth traits and vegetation indices (VIs), the genetic correlation coefficients between the traits were calculated. Finally, the eucalypt clones were ranked by integrating a selection index of traits, and the superior genotypes were selected and their genetic gain predicted. The results showed a high accuracy of the tree height (H) extracted from the digital aerial photogrammetry (DAP) point cloud based on UAV images (R2 = 0.91, and RMSE = 0.56 m), and the accuracy of estimating the diameter at breast height (DBH) was R2 = 0.71, and RMSE = 0.75 cm. All the extracted traits were significantly different within the tree species and among the clones. Except for the crown width (CW), the clonal repeatability ( of the traits were all above 0.9, and the individual repeatability values ( were all above 0.5. The genetic correlation coefficient between the tree growth traits and VIs fluctuated from 0.3 to 0.5, while the best clones were EA14-15, EA14-09, EC184, and EC183 when the selection proportion was 10%. The purpose of this study was to construct a technical framework for phenotypic traits extraction and genetic analysis of trees based on unmanned aerial stereo photography point clouds and high-resolution multispectral images, while also exploring the application potential of this approach in the selective breeding of eucalypt clones.
... Nevertheless, there are key limitations in scope and technical aspects for this implementation that should be highlighted. First, to extend this framework to other phenotypic characteristics and crops, the target-trait must be both (i) quantifiable with HTFP (Pauli et al., 2016;White et al., 2012) using imaging capture or other methods (Kelly et al., 2016;Rasmussen et al., 2016), and (ii) represented in G2P models in a way that the relevant G x E x M interactions are captured through drivers and processes of the climate-soil-plant continuum (Rotter et al., 2015). This limits the scope of possibilities because the ability to precisely and cost-effectively differentiate relevant traits with automated proximal-sensing under field conditions (Araus and Cairns, 2014), understand their underlying causes or secondary traits in targeted trials (Chenu et al., 2018) and accurately represent these as model parameters (Parent and Tardieu, 2014;Rotter et al., 2015) largely differs across phenotypical traits and crop species. ...
... Spectral reflectance indices were used as indicators of plant status under different stresses due to their close relationship with traits, such as leaf chlorophyll content, canopy conductance, leaf temperature, leaf area index and leaf senescence (White et al. 2012;Rossini et al. 2013;Panigada et al. 2014;Cerrudo et al. 2017). Some vegetative indices obtained via remote sensing have the advantage to exclude environmental variation associated with the time of sampling for some physiological traits (Cossani, Pietragalla, and Reynolds 2012). ...
Heat and drought stresses negatively affect maize (Zea mays L.) productivity. We aimed to identify the genetic basis of tolerance to heat stress (HS) and combined heat and drought stress (HS+DS) and compare how QTL and whole genome selection (GS) could be leveraged to improve tolerance to both stresses. A set of 97 testcross hybrids derived from a maize bi-parental doubled-haploid population was evaluated during the summer seasons of 2014, 2015, and 2016 in Ciudad Obregon, Sonora, Mexico, under HS and HS+DS. Grain yield (GY) reached 5.7 t ha⁻¹ under HS and 3.0 t ha⁻¹ under HS+DS. Twenty-six QTL were detected across six environments, with LOD scores ranging from 2.03 to 3.86; the QTL explained 8.6% to 18.6% of the observed phenotypic variation. Hyperspectral biomass and structural index (HBSI) had higher genetic correlation with GY for HS (r = 0.97) and HS+DS (r = 0.74), relative to the correlation with crop water mass or greenness indices. Genetic correlations between GY and canopy temperature for HS (r = −0.89) and HS+DS (r = −0.75) or vegetation indices, along with clusters of QTL in bins 1.02, 1.05, and 2.05, underline the importance of these genomic areas for secondary traits associated with general vigor and greenness. Prediction accuracy of the model used for GS had values below those found in previous studies. We found a high-yielding hybrid that was tolerant to HS and HS+DS.
... To meet the global increasing demand for food, plant breeders have been dedicated to crop genetic improvement and developing new genotypes of higher yield potential (Ray and Satya 2014). Large numbers of new varieties are usually planted in plots of experiment field to assess their performance in terms of yield and productivity (White et al. 2012). Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing, characterized by high spatial and spectral resolution, has been widely applied to identify varieties of higher yield potential before harvest (Montesinos-López et al. 2017). ...
The growth stage best suitable for wheat yield phenotyping has been a hot topic. This study provides a fresh insight into it, from the perspective of spectral correlation between wheat genotype replications. A number of 340 wheat genotypes and their replication were distributed in separate parts (west and east) of an experiment field (2019–2020) in Ashland, Kansas, USA. Unmanned aerial vehicle based hyperspectral images (400–100 nm) of the experiment field were taken over the late growing season, on 29 May 2020, 5 June 2020, and 12 June 2020, respectively. For each narrow spectral band, we calculated a coefficient of determination (R²) between the reflectance of genotype replications. Results suggest that R² is relatively stable within visible spectrum (450–700 nm) and within near-infrared (NIR) spectrum (770–1000 nm), though it tends to be higher for the visible bands. Moreover, while the R² of the visible bands decreases across the three dates, it increases for the near-infrared bands. These findings suggest that genetic information is better reflected in visible reflectance than in near-infrared reflectance. Among the three dates, the one when highest intra-genotype spectral correlation over visible spectrum was observed might be the best timing to discriminate yield levels of different genotypes.
... Through the use of unoccupied aerial vehicles (UAVs) and other systems, aerial imaging can reliably and cost-effectively measure high-throughput phenotypes (HTPs) for all experimental plots in the field across the growing season (White et al. 2012;Andrade-Sanchez et al. 2014;Sagan et al. 2019;Sun et al. 2021). A widely studied class of aerial image HTPs are vegetation indices (VI) that include the normalized difference vegetation index (NDVI) (Gitelson et al. 2002;Hunt et al. 2013). ...
Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index (NDVI) was measured by a multi-spectral MicaSense camera and ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multi-trait model, a two-stage approach that quantified NDVI local environmental effects (NLEE) was proposed. Firstly, NLEE were separated from additive genetic effects over the growing season using two-dimensional spline (2DSpl), separable autoregressive (AR1) models, or random regression models (RR). Secondly, the NLEE were leveraged within agronomic trait genomic best linear unbiased prediction (GBLUP) either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of NLEE across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields (G2F) hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to all baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2DSpl NLEE were most correlated to the soil parameters and grain yield 2DSpl effects. Simulation of field effects demonstrated improved specificity for RR models. In summary, NLEE increased experimental accuracy and understanding of field spatio-temporal heterogeneity.
... Worldwide demand for food has increased dramatically owing to the constraints of arable land area and the increasing global population. Some studies suggest that the yield of agricultural systems must double by 2050 to meet the growing food demand of the worldwide population (White et al., 2012;Holman et al., 2016). Staple crops, such as rice (Oryza sativa), maize (Zea mays), and wheat (Triticum aestivum), have a limited scope for increasing yields and high requirements for irrigation systems, which results in high production costs and low potential. ...
Plant nitrogen content (PNC) is an important indicator to characterize the nitrogen nutrition status of crops, and quickly and efficiently obtaining the PNC information aids in fertilization management and decision-making in modern precision agriculture. This study aimed to explore the potential to improve the accuracy of estimating PNC during critical growth periods of potato by combining the visible light vegetation indices (VIs) and morphological parameters (MPs) obtained from an inexpensive UAV digital camera. First, the visible light VIs and three types of MPs, including the plant height (H), canopy coverage (CC) and canopy volume (CV), were extracted from digital images of the potato tuber formation stage (S1), tuber growth stage (S2), and starch accumulation stage (S3). Then, the correlations of VIs and MPs with the PNC were analyzed for each growth stage, and the performance of VIs and MPs in estimating PNC was explored. Finally, three methods, multiple linear regression (MLR), k-nearest neighbors, and random forest, were used to explore the effect of MPs on the estimation of potato PNC using VIs. The results showed that (i) the values of potato H and CC extracted based on UAV digital images were accurate, and the accuracy of the pre-growth stages was higher than that of the late growth stage. (ii) The estimation of potato PNC by visible light VIs was feasible, but the accuracy required further improvement. (iii) As the growing season progressed, the correlation between MPs and PNC gradually decreased, and it became more difficult to estimate the PNC. (iv) Compared with individual MP, multi-MPs can more accurately reflect the morphological structure of the crop and can further improve the accuracy of estimating PNC. (v) Visible light VIs combined with MPs improved the accuracy of estimating PNC, with the highest accuracy of the models constructed using the MLR method (S1: R 2 = 0.79, RMSE=0.27, NRMSE=8.19%; S2:R 2 = 0.80, RMSE=0.27, NRMSE=8.11%; S3: R 2 = 0.76, RMSE=0.26, NRMSE=8.63%). The results showed that the combination of visible light VIs and morphological information obtained by a UAV digital camera could provide a feasible method for monitoring crop growth and plant nitrogen status.
... The 3D laser point cloud can also provide simulation methods and basic data for agricultural crop phenotyping research [17]. Many scholars at home and abroad have poured into the field of high-throughput field phenotyping based on the 3D laser point cloud [14,18,19]. For example, Miao et al. [20] conducted a series of studies in the field of maize point cloud stem and leaf segmentation and ear recognition. ...
Three-dimensional (3D) laser point cloud technology is an important research method in the field of agricultural remote sensing research. The collection and processing technology of terrestrial light detection and ranging (LiDAR) point cloud of crops has greatly promoted the integration of agricultural informatization and intelligence. In a smart farmland based on 3D modern agriculture, the manager can efficiently and conveniently achieve the growth status of crops through the point cloud collection system and processing model integrated in the smart agricultural system. To this end, we took field maize as the research object in this study and processed four sets of field maize point clouds, named Maize-01, Maize-02, Maize-03, and Maize-04, respectively. In this research, we established a field individual maize segmentation model with the density-based clustering algorithm (DBSCAN) as the core, and four groups of field maize were used as research objects. Among them, the value of the overall accuracy (OA) index, which was used to evaluate the comprehensive performance of the model, were 0.98, 0.97, 0.95, and 0.94. Secondly, the multi-condition identification method was used to separate different maize organ point clouds from the individual maize point cloud. In addition, the organ stratification model of field maize was established. In this organ stratification study, we take Maize-04 as the research object and obtained the recognition accuracy rates of four maize organs: tassel, stalk, ear, and leaf at 96.55%, 100%, 100%, and 99.12%, respectively. We also finely segmented the leaf organ obtained from the above-mentioned maize organ stratification model into each leaf individual again. We verified the accuracy of the leaf segmentation method with the leaf length as the representative. In the linear analysis of predicted values of leaf length, was 0.73, was 0.12 m, and was 0.07 m. In this study, we examined the segmentation of individual crop fields and established 3D information interpretations for crops in the field as well as for crop organs. Results visualized the real scene of the field, which is conducive to analyzing the response mechanism of crop growth and development to various complex environmental factors.
... The high-throughput phenotyping platform, which enables the accurate assessment of a large number of field plots with a variety of measures in a short period of time, simplifies the routine quantification of crop development, physiology, and phenological characteristics (White et al., 2012;Araus and Cairns, 2014). These data provide a useful framework for addressing phenotypic bottlenecks in plant breeding (Furbank and Tester, 2011;Araus and Cairns, 2014;Kumar et al., 2015). ...
The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.
... Originated under diverse agroecological conditions, these accessions represent an important genetic resource for biotic and abiotic stress resistance, which is the key to genetic gain and crop improvement (Zanklan, 2003). Indeed, in this study, a great diversity of ELS and LLS symptoms was observed among the genotypes that could be attributed to their genetic ability to respond differently to infection by the casual pathogens (White et al., 2012). For example, genotypes 73-33, Zam-MGV-8:201909, Mwi-ICGV-SM 01711:201909, Zam-ICGV-SM-07599:201909, Gha-GAF 1723:201909, Oug-ICGV 90099:201909, Mal-ICGV 02271:201909, Sen-SERENUT 10R:201909, and GhaII-AZIVIVI:201909 were moderately tolerant to the leaf spot diseases. ...
Early Leaf Spot (ELS) caused by the fungus Passalora arachidicola and Late Leaf Spot (LLS) also caused by the fungus Nothopassalora personata, are the two major groundnut ( Arachis hypogaea L.) destructive diseases in Ghana. Accurate phenotyping and genotyping to develop groundnut genotypes resistant to Leaf Spot Diseases (LSD) and to increase groundnut production is critically important in Western Africa. Two experiments were conducted at the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute located in Nyankpala, Ghana to explore the effectiveness of using RGB-image method as a high-throughput phenotyping tool to assess groundnut LSD and to estimate yield components. Replicated plots arranged in a rectangular alpha lattice design were conducted during the 2020 growing season using a set of 60 genotypes as the training population and 192 genotypes for validation. Indirect selection models were developed using Red-Green-Blue (RGB) color space indices. Data was collected on conventional LSD ratings, RGB imaging, pod weight per plant and number of pods per plant. Data was analyzed using a mixed linear model with R statistical software version 4.0.2. The results showed differences among the genotypes for the traits evaluated. The RGB-image method traits exhibited comparable or better broad sense heritability to the conventionally measured traits. Significant correlation existed between the RGB-image method traits and the conventionally measured traits. Genotypes 73–33, Gha-GAF 1723, Zam-ICGV-SM 07599, and Oug-ICGV 90099 were among the most resistant genotypes to ELS and LLS, and they represent suitable sources of resistance to LSD for the groundnut breeding programs in Western Africa.
Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.
With abundant available genomic data, genomic selection has become routine in many plant breeding programs. Multispectral data captured by UAVs showed potential for grain yield prediction in many plant species using machine learning; however, the possibilities of utilizing this data to augment genomic prediction models still need to be explored. We collected HTP multispectral data in a genotyped multi-environment large-scale field trial using two cost-effective cameras to fill this gap. We tested back-to-back the prediction ability of GY prediction models, including genomic (G matrix), multispectral-derived (M matrix), and environmental (E matrix) relationships using BLUP methodology in single and multi-environment scenarios. We discovered that M allows for GY prediction comparable to the G matrix and that models using both G and M matrices show superior accuracies and errors compared with G or M alone, both in single and multi-environment scenarios. We showed that the M matrix is not entirely environment-specific, and the genotypic relationships become more robust with more data capture sessions over the season. We discovered that the optimal time for data capture occurs during grain filling and that camera bands with the highest heritability are important for GY prediction using the M matrix. We showcased that GY prediction can be performed using only an RGB camera, and even a single data capture session can yield valuable data for GY prediction. This study contributes to a better understanding of multispectral data and its relationships. It provides a flexible framework for improving GS protocols without significant investments or software customization.
The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose an original approach that combines nonlinear mixed-effects model (NLME) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth. These tools are widely used in many fields but very rarely in plant biology. During model formulation, a nonlinear mechanistic function describes the shape of growth, and random effects describe genetic and environmental effects and their variability. Genetic relationships among the varieties were also integrated into the model using a kinship matrix. The SAEM algorithm was chosen as an efficient alternative to MCMC methods, which are more commonly used in the domain. It was implemented to infer the expected growth patterns in the analyzed population and the expected curves for each variety through a maximum-likelihood and a maximum-a-posteriori approaches, respectively. The obtained estimates can be used to predict the growth curves for each variety. We illustrate the strengths of the proposed approach using simulated data and soybean plant growth data obtained from a soybean cultivation experiment conducted at the Arid Land Research Center, Tottori University. In this experiment, plant height was measured daily using drones, and the growth was monitored for approximately 200 soybean cultivars for which whole-genome sequence data were available. The NLME approach improved our understanding of the determinants of soybean growth and can be successfully used for the genomic prediction of growth pattern characteristics.
Author summary
Nonlinear mechanistic models are useful for modeling animal and plant growth; however, their parameters are influenced by both genetic and environmental factors. If the same model can be applied to data with different genetic and environmental factors by allowing parameter variations, it can be used to understand, predict, and control the genetic and environmental influences of growth models based on parameter variation. In this study, we propose a statistical method for integrating a nonlinear mixed-effects model with a nonlinear mechanistic model. The simulation and real data analysis results show that the proposed method was effective in modeling the growth of genetically different soybean varieties under different drought conditions. The usefulness of the proposed method is expected to increase, as high-throughput measurements provide growth data for a large number of genotypes in various environments.
Rosnące zapotrzebowanie na żywność jak również uwarunkowania ekonomiczne stawiają przed hodowlą wymaganie ciągłego podnoszenia produktywności odmian wprowadzanych do uprawy. Zielona rewolucja zaowocowała wprowadzeniem form karłowatych i na półwiecze zdeterminowała mechanizmy podnoszenia plonowania przez zwiększanie indeksu plonowania (HI), które wraz z wysoką kulturą rolną pozwoliły na osiągnięcie plonów pszenicy wyższych niż 10 t/ha. Osiągnięto optymalną wielkość HI (dla pszenicy ≈ 0,64), tak więc potencjał plonowania związany z tym parametrem został wyczerpany i w związku z tym pojawiło się pytanie jakie fenotypy zbóż zagwarantują dalszy wzrost plonów w nadchodzących dekadach, dla których prognozowane jest zjawisko pogłębiającej się suszy glebowej. W artykule przedstawiono wyniki modelowania ideotypów pszenicy wygenerowanych w ośrodku badawczo-hodowlanym w Rothamsted (UK) przy użyciu mechanistycznego modelu Sirius rozszerzonego o algorytm ewolucyjny (GA-SA) i prognozy klimatyczne (HadCM3). Zarysowano biochemiczne i fizjologiczne uwarunkowania odporności roślin na suszę oraz poruszono zagadnienie efektywnego wykorzystania wody przez rośliny uprawne.
For many crops, the genomics revolution has given hope that breeding would become easier, faster, and more efficient. Relevant phenotyping is now the main bottleneck and new technologies provide opportunities for easier, faster, more sensitive, and more informative phenotyping. However, the phenotyping agenda must be driven by scientific questions rather than by a technological push, especially for complex constraints, such as drought. In this chapter, we provide a viewpoint on phenotyping and what it should take into account. Phenotyping is a full-fledge research effort, calling for a multidisciplinary effort between technology providers and several research disciplines, and which needs to address the issue of linking scales. Two phenotyping platforms are described; a lysimetric platform (LysiField) to assess the patterns of plant water use and relate these to grain yield, and an imaging platform (LeasyScan) to characterize crop canopy traits responsible for water savings. In both cases, the chapter discusses the thought process and the hypotheses around key traits for drought adaptation that were put in the development of these platforms. The chapter concludes with perspectives on the integration of high-throughput phenotyping (HTP) technology with breeding, starting with an analysis of the cost as a prerequisite to decide on its usage and adoption in breeding. It takes a few examples of current opportunities in the domain of imaging, trying to bring closer together what the technology can bring and what breeding pragmatically needs. In conclusion, while new technologies provide opportunities to make phenotyping easier, faster, better, cheaper, the risk of becoming the end that justifies the means can be avoided by driving the technology with research questions, made possible through a cross-discipline approach between genetics, breeding, modeling, engineering, physiology, and statistics.KeywordsMulti-discipline traitDroughtBreedingGenetic gainTranspiration
Genotype‐by‐environment‐by‐management (G × E × M) interactions for crop productivity represent both challenges and opportunities for long‐term crop improvement. They need to be understood and harnessed to accelerate crop improvement to improve our chances of achieving the targets for sustainable agriculture and global food security that are required to enable sustainable development ( https://sdgs.un.org/goals ). Experimental efforts have emerged to quantify their importance, study their genetic and ecophysiological bases, and support efforts to exploit nascent opportunities. The large and complex G × E × M factorial limits the scope and feasibility for purely experimental approaches at all stages of crop improvement programs. However, iterative experimental modeling approaches, combined with advances in genomics, enviromics, phenomics, simulation, and prediction methodologies, offer a range of opportunities, covering both genomic prediction for breeding and agronomic prediction for enhancing the realization of on‐farm productivity targets and managing risk. There has been limited coordination of breeding and agronomic prediction efforts to date. We consider the different perspectives of each area and advances toward their integration. This aims to bring an “end‐to‐end” perspective to the development of G × E × M prediction methodology for sustainable crop improvement; from the creation of new genotypes in breeding programs to their use in combination with agronomic management strategies within on‐farm production systems. We draw on the historical and current efforts to improve yield productivity of maize ( Zea mays L.) for the US Corn Belt as a source of examples. Extensions to and examples from other crops and geographies are also considered. Opportunities for G × E × M prediction to enable circular agriculture strategies, that balance crop productivity and sustainable resource use, are identified as potential targets for the future.
Soybean is globally a promising oilseed legume providing quality protein and edible oil for human consumption and animal feed, besides supplying minerals and antioxidant lecithin for human health benefits. The crop is climate-sensitive. The drought stress condition occurs particularly during seed fill stage of the crop construing the stage critical for the reproductive phase when yield levels get much reduced. The set of drought conditions occurring during terminal developmental stage of the crop is the target population of environments which is crucial for orienting the objectives of soybean improvement program. It is the well-coordinated expression of complex physiological processes and biochemical pathways in a genotype that can be captured under well-defined water-limited conditions, using novel phenotyping techniques of trait extraction and recent molecular system of gene editing. Together, they will enhance climate resilience in drought-tolerant soybeans enduring sustainable productivity for ensured nutritional food security.
Bambara groundnut ( Vigna subterranea L. Verdc; BGN) is an important legume grown mainly by small-scale subsistence farmers in sub-Saharan Africa, in parts of Thailand and Indonesia. It has a high concentration of seed carbohydrate (55–70%), protein (17–25%), fat (1.4–12%) and dietary fibre (5.2–6.4%). A range of biotic and abiotic stresses together with socio-economic constraints affect its productivity, yield and quality. The changing climate and a growing world population are putting pressure on food production, as world food supply is heavily reliant on few crops. As such, there is a need to broaden crop usage and increase yield of minor crops such as BGN. Improvements in production can potentially be achieved by a combination of advanced phenotyping, genotyping, environmental characterization and overall management approaches. Breeding for advanced lines is complicated by overreliance on landraces. This review aims to provide the current status of BGN production, production constraints and approaches to overcome these, as well as its grain composition and nutritional value. It further discusses and elaborates on potentially available opportunities for overall improvement, so that BGN, like all neglected crops, can play a valuable role in world food security. Efforts should be intensified to improve the overall utilization of BGN and its constituents to make it an economically viable crop.
Wheat yields are stagnating or declining in many regions of the planet, requiring efforts to improve the light conversion efficiency, i.e., radiation use efficiency (RUE). RUE is a key trait in plant physiology because it links light capture and primary metabolism with biomass accumulation and yield. High-throughput phenotyping (HTP) was used among a population of field grown wheat with variation in RUE and photosynthetic traits to build predictive models of RUE, biomass and intercepted photosynthetically active radiation (IPAR). The use of remote sensing models predicted RUE with up to 70% accuracy compared to ground truth data. Wheat yield can be defined as the product of solar radiation intercepted throughout the crop cycle, radiation use efficiency and harvest index. Photosynthesis is a central component of RUE but normally measured in the upper layers of the canopy where light conditions are saturating. Significant relationships were found between light saturated photosynthetic rates measured at initiation of booting in the top, middle and bottom layers of the canopy and yield. These findings indicate that there is an opportunity for yield improvement if we consider the requirements of photosynthesis in the middle and bottom layers of wheat canopies where conditions are not light saturating. The study of photosynthesis in the field is constrained by low throughput and lack of integrative measurements at canopy level. Partial least squares regression (PLSR) modelling was used to build predictive models of photosynthetic, biophysical and biochemical traits at the top, middle and bottom layers of wheat canopies. The combined layer model predictions performed better than individual layer predictions. Using HTP allowed us to increase phenotyping capacity 30-fold compared to conventional phenotyping methods and our models can be used to screen varieties for high and low RUE. There is clear consensus in the physiological and breeding communities that improving RUE will be key to boost wheat yield. In the previous years of RUE research little has been explored on the role of root biomass accumulation and its interaction with aboveground biomass accumulation, RUE and yield. Strong positive associations were found between above and belowground biomass accumulation with RUE and root biomass during the vegetative period, and negative associations between yield components and root biomass accumulation, suggesting there is a coordination between roots and shoot in the vegetative period to maximize growth. However, if too much energy is invested in root biomass this will have an effect in decreasing aboveground biomass during grain filling. More research will be needed to explore new hypothesis in the field that accounts the effect of root biomass in canopy RUE and yield.
Pre-plant soil NO3--N tests and petiole NO3--N analysis are bases for Upland cotton (Gossypium hirsutum L.) N management in the western USA. Alternative approaches include proximal multispectral reflectance sensing and chlorophyll meter readings. Our objective was to determine if spectral reflectance and chlorophyll meter measurements correlate with cotton leaf N and biomass. Urea ammonium nitrate was applied after emergence and with low energy precision (LEPA) center-pivot, surface or subsurface drip irrigation water up to peak bloom. Multispectral reflectance readings 0.5 m above the canopy, chlorophyll meter readings, and biomass samplings were taken at early squaring, early bloom, and peak bloom for 3 site-years in Lubbock, TX and Ropesville, TX. Green vegetative indices (GVI) and green normalized difference vegetative indices (GNDVI) calculated from reflectance data generally correlated better with leaf N and leaf N accumulation than did red vegetative indices (RVI) and red normalized difference vegetative indices (RNDVI). Biomass and lint yield correlated more often with red-based indices than green-based indices. Chlorophyll meter readings correlated with leaf N as often as GVI and GNDVI did. Biomass, however was poorly related to chlorophyll meter readings. These results demonstrate the effectiveness of GVI, GNDVI, and chlorophyll meter readings in assessing leaf N, and RVI and RNDVI in assessing cotton biomass. However, we recommend converting vegetative indices or chlorophyll meter readings to sufficiency indices, which are calculated from indices or readings relative to well-fertilized plots. Sufficiency indices were able to successfully predict little or no need for in-season N fertilizer in the low-yielding 2000 crops (sufficiency index > 0.95), and predicted greater need of N fertilizer in the high-yielding 2001 crop (sufficiency index < 0.95).
Nitrogen fertilizer management in subsurface drip irrigation (SDI) systems for cotton (Gossypium hirsutum L.) can be very efficient when N is injected with the irrigation water (fertigated) on a daily basis. However, the daily rates and total amounts of N fertigation are uncertain. Normalized difference vegetative index (NDVI), calculated from weekly canopy reflectance measurements can guide N management in SDI cotton. The objective of this 3-yr study (2007-2009) on an Acuff sandy clay loam (fine-loamy, mixed, superactive, thermic Aridic Paleustolls) near Lubbock, TX, was to test two canopy reflectance-based strategies for estimating and adjusting injection rates of urea ammonium nitrate (UAN) fertilizer between first square and early mid-bloom. We also evaluated three N rates; 50, 100, and 150% of the soil test-based N recommendation for a 1400 kg lint ha(-1) yield goal. In the reflectance-based N strategy-1 (RN1), UAN was injected starting at first square at 50% of the soil test N rate. When NDVI in RN1 fell significantly below NDVI of plots with 100% soil test N, the N injection rate was increased to match the injection rate of the 100% soil test plots. The reflectance-based N strategy-2 (RN2) had an initial N injection rate equal to that of the 100% soil test N, and was raised to match the 150% soil test N based on NDVI. Nitrogen rates for the RN1 averaged across 3 yr were 22 kg N ha(-1) less, or 31% less than the soil test treatment, without hurting lint or seed yields. In 2007, N rates with RN2 were 11 kg N ha(-1) higher than the soil test N rate, without any yield benefit. Economic optimum N rates for lint production ranged from 23 kg N ha(-1) in 2009 to 75 kg N ha(-1) in 2008.
This paper presents the design of an autonomous unmanned helicopter system for low- Altitude remote sensing. The proposed concepts and methods are generic and not limited to a specific helicopter. The development was driven by the need for a dependable, modular, and affordable system with sufficient payload capacity suitable for both research and real-world deployment. The helicopter can be safely operated without a backup pilot in a contained area beyond visual range. This enables data collection in inaccessible or dangerous areas. Thanks to its terrain following and obstacle avoidance capability, the system does not require a priori information about terrain elevation and obstacles. Missions are specified in state diagrams and flight plans. We present performance characteristics of our system and show results of its deployment in real-world scenarios. We have successfully completed several dozen infrastructure inspection missions and crop monitoring missions facilitating plant phenomics studies.
This review addresses the challenges and progress in sensor development and radiometric correction for agricultural applications with particular emphasis on activities within the U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS). Examples of sensor development include on-site development of sensors and platforms, participation in cooperative research and development agreements (CRADA) with commercial companies, and membership on NASA science teams. Examples of progress made in sensor radiometric correction suitable for agriculture are presented for both laboratory and field environments. The direction of future sensor development includes integrated sensors and systems, sensor standardization, and new sensor technologies measuring fluorescence and soil electrical conductivity, and utilizing LIght Detection and Ranging (lidar), hyperspectral, and multiband thermal wavelengths. The upcoming challenges include definition of the core spectral regions for agriculture and the sensor specifications for a dedicated, orbiting agricultural sensor, determination of an operational approach for reflectance and temperature retrieval, and enhanced communication between image providers, research scientists, and users. This review concludes with a number of avenues through which USDA could promote sensor development and radiometric correction for agricultural applications. These include developing a network of large permanent calibration targets at USDA ARS locations; investing in new technologies; pooling resources to support large-scale field experiments; determining ARS-wide standards for sensor development, calibration, and deployment; and funding interagency agreements to achieve common goals.
Remote sensing research indicates that leaf-canopy temperatures are related to plant water stress. Leaf-canopy temperatures can be rapidly monitored remotely by the IR thermometer. Ground-level IR sensed leaf-canopy temperatures were evaluated as a fast comparative assay of dehydration avoidance (maintenance of higher leaf water potential) in wheat breeding materials, subjected to soil moisture stress.In three separate field tests, during a period of moisture stress, midday temperatures of fully developed plant canopies were correlated with leaf water potentials across various wheat strains. Lower canopy temperatures were indicative of higher leaf water potentials. Correlation coefficients became larger and more significant as streess progressed. In one test, where sufficient data were available, leaf temperature was also significantly and positively correlated with leaf diffusive resistance, across strains.While site (and instrument) variation in leaf temperatures, as measured across replicated plots of a given genotype, ranged within about 1–2°C, extreme differences among various genetic materials ranged within 4–8°C, with an LSD of 1.1 – 1.9°C, depending on the test and stress intensity. If non-replicated tests were used, the resolution of genetic differences in leaf temperatures was improved by transforming temperature measurements to the percentage of a replicated check cultivar.
Spectral indices as a selection tool in plant breeding could improve genetic gains for different important traits. The objectives of this study were to assess the potential of using spectral reflectance indices (SRI) to estimate genetic variation for in-season biomass production, leaf chlorophyll, and canopy temperature (CT) in wheat (Triticum aestivum L.) under irrigated conditions. Three field experiments, GHIST (15 CIMMYT globally adapted historic genotypes), RILs1 (25 recombinant inbred lines (RILs)), and RILs2 (36 RILs) were con- ducted under irrigated conditions at the CIMMYT research station in northwest Mexico in three different years. Five SRI were evaluated to differentiate genotypes for biomass production. In general, genotypic variation for all the indices was significant. Near infrared radiation (NIR)-basedindicesgavethehighestlevelsofassociationwithbiomass production and the higher associations were observed at heading and grainfilling, rather than at booting. Overall, NIR-based indices were more consistent and differentiated biomass more effectively compared to the other indices. Indices based on ratio of reflection spectra cor-
The objectives of this study were to assess the potential of using spectral reflectance indices (SRI) as an indirect selection tool to dif- ferentiate spring wheat (Triticum aestivum L.) genotypes for grain yield under irrigated conditions. This paper demonstrates only the first step in using the SRI as indirect selection criteria by reporting genetic variation for SRI among genotypes, the effect of phenology and year on SRI and their interaction with genotypes, and the correlations be- tween SRI and grain yield and yield components of wheat. Three field experiments—15 CIMMYT globally adapted genotypes (GHIST), 25 random F3-derived lines (RLs1), and 36 random F3-derived lines (RLs2)—were conducted under irrigated conditions at the CIMMYT research station in northwest Mexico in three different years. Five pre- viously developed SRI (photochemical reflectance index (PRI), water index (WI), red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), simple ratio (SR)) and two newly calculated SRI (normalized water index-1 (NWI-1) and normalized water index-2 (NWI-2)) were evaluated in the experi- ments. In general, genotypic variation for all the indices was signifi- cant. Near infrared radiation (NIR)-based indices (WI, NWI-1, NWI-2) gave the highest levels of association with grain yield during the 3 yr of the study. A clear trend for higher association between grain yield and the NIR-based indices was observed at heading and grainfilling than at booting. Overall, NIR-based indices were more consistent and differentiated grain yield more effectively compared to the other indi- ces. The results demonstrated the potential of using SRI as a tool in breeding programs for selecting for increased genetic gains for yield.
Pre-plant soil NO 3--N tests and petiole NO 3--N analysis are bases for Upland cotton (Gossypium hirsutum L.) N management in the western USA. Alternative approaches include proximal multispectral reflectance sensing and chlorophyll meter readings. Our objective was to determine if spectral reflectance and chlorophyll meter measurements correlate with cotton leaf N and biomass. Urea ammonium nitrate was applied after emergence and with low energy precision (LEPA) center-pivot, surface or subsurface drip irrigation water up to peak bloom. Multispectral reflectance readings 0.5 m above the canopy, chlorophyll meter readings, and biomass samplings were taken at early squaring, early bloom, and peak bloom for 3 site-years in Lubbock, TX and Ropesville, TX. Green vegetative indices (GVI) and green normalized difference vegetative indices (GNDVI) calculated from reflectance data generally correlated better with leaf N and leaf N accumulation than did red vegetative indices (RVI) and red normalized difference vegetative indices (RNDVI). Biomass and lint yield correlated more often with red-based indices than green-based indices. Chlorophyll meter readings correlated with leaf N as often as GVI and GNDVI did. Biomass, however was poorly related to chlorophyll meter readings. These results demonstrate the effectiveness of GVI, GNDVI, and chlorophyll meter readings in assessing leaf N, and RVI and RNDVI in assessing cotton biomass. However, we recommend converting vegetative indices or chlorophyll meter readings to sufficiency indices, which are calculated from indices or readings relative to well-fertilized plots. Sufficiency indices were able to successfully predict little or no need for in-season N fertilizer in the low-yielding 2000 crops (sufficiency index > 0.95), and predicted greater need of N fertilizer in the high-yielding 2001 crop (sufficiency index < 0.95).
Recent improvement in crop stimulation techniques and in understanding of crop genetics suggest the possibility of integrating genetic information on physiological traits into crop simulation models. By using known genotypes, rather than empirically fitted cultivar-specific coefficients, a simulation model should permit more explicit testing of hypotheses concerning the genetic basis of adaptation of cultivars to different environments or production systems. This paper describes and evaluates GeneGro, a version of the dry bean (Phaseolus vulgaris L.) crop simulation model BEANGRO version 1.01 modified to incorporate effects of seven genes affecting phenology, growth habit, and seed size: Ppd, Hr, Fin, Fd, and Ssz- I, and two more genes for seed size inferred from indirect evidence. Thirty cultivars were calibrated for BEANGRO using data Crum 14 trials conducted in Colombia, Guatemala, Mexico, and Florida. The resulting cultivar-specific coefficients of BEANGRO were replaced with information on specific genotypes of cultivars to create the gene-based model. With cultivar differences specified only by the seven genes, GeneGro explained 31% of observed variation for seed yield, 58% for seed weight, 84% for days to flowering, 85% for days to maturity, 52% for maximum leaf area index, and 36% for canopy dry weight at maturity, but 0% for harvest index. In testing the effectiveness of GeneGro after overall trial and cultivar effects were accounted for through regression analysis, all simulated data except for seed weight showed significant relations with observed data (P ≤ 0.01). Our results indicate that for certain traits surprisingly few genes must be characterized to simulate cultivar differences as accurately as with the BEANGRO model. Furthermore, they suggest a potential for developing models similar to GeneGro for studying the effects of genes on adaptation in other crops.
mass, and grain yield of corn with hyperspectral reflec- tance at 3 m above the canopy. Ma et al. (2001) esti- Multispectral reflectance of crop canopies has potential as an in- mated soybean (Glycine max (L.) Merr.) yield with season indicator of N status in cotton (Gossypium hirsutum L.). The objectives of this study were to correlate leaf N with reflectance at NDVI calculations of in-season measurements of spec- 16 wavebands from 450 to 1700 nm and to assess the effect of N tral reflectance 2 m above the ground. Xue et al. (2004) fertilization on vegetative ratio indices using two bands of reflectance. used multispectral reflectance at 2 m above the canopy We also compared regressions of leaf N on ratio indices with partial to monitor leaf N in rice (Oryza sativa L.). least squares (PLS) regression using reflectance at 16 wavebands. Assessing N concentration of cotton with spectral re- Reflectance was measured at 50 cm above the canopy at 135 points flectance has in many cases been confined to analysis of in a 14-ha field of irrigated cotton at early squaring in the Texas High individual leaves. Lough and Varco (2001) used spectral Plains in 2002 and at an 80-cm height in 2003 and 2004. Leaf N reflectance to assess N and K status of cotton leaves had weak, negative correlation with green reflectance in all 3 yr. harvested from the field. They reported that reflectance Normalized difference vegetative indices (NDVIs) using red (670 nm)
1] Leaf pigment content and composition provide important information about plant physiological status. Reflectance measurements offer a rapid, nondestructive technique to estimate pigment content. This paper describes a recently developed three-band conceptual model capable of remotely estimating total of chlorophylls, carotenoids and anthocyanins contents in leaves from many tree and crop species. We tuned the spectral regions used in the model in accord with pigment of interest and the optical characteristics of the leaves studied, and showed that the developed technique allowed accurate estimation of total chlorophylls, carotenoids and anthocyanins, explaining more than 91%, 70% and 93% of pigment variation, respectively. This new technique shows a great potential for noninvasive tracking of the physiological status of vegetation and the impact of environmental changes. (2006), Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves, Geophys. Res. Lett., 33, L11402, doi:10.1029/ 2006GL026457.
Remote sensing is a key technology for precision agriculture to assess actual crop conditions. Commercial, high-spatial-resolution imagery from aircraft and satellites are expensive so the costs may outweigh the benefits of the information. Hobbyists have been acquiring aerial photography from radio-controlled model aircraft; we evaluated these very-low-cost, very high-resolution digital photography for use in estimating nutrient status of corn and crop biomass of corn, alfalfa, and soybeans. Based on conclusions from previous work, we optimized an aerobatic model aircraft for acquiring pictures using a consumer-oriented digital camera. Colored tarpaulins were used to calibrate the images; there were large differences in digital number (DN) for the same reflectance because of differences in the exposure settings selected by the digital camera. To account for differences in exposure a Normalized Green–Red Difference Index [(NGRDI =(Green DN −Red DN)/(Green DN +Red DN)] was used; this index was linearly related to the normalized difference of the green and red reflectances, respectively. For soybeans, alfalfa and corn, dry biomass from zero to 120gm−2 was linearly correlated to NGRDI, but for biomass greater than 150gm−2 in corn and soybean, NGRDI did not increase further. In a fertilization experiment with corn, NGRDI did not show differences in nitrogen status, even though areas of low nitrogen status were clearly visible on late-season digital photographs. Simulations from the SAIL (Scattering of Arbitrarily Inclined Leaves) canopy radiative transfer model verified that NGRDI would be sensitive to biomass before canopy closure and that variations in leaf chlorophyll concentration would not be detectable. There are many advantages of model aircraft platforms for precision agriculture; currently, the imagery is best visually interpreted. Automated analysis of within-field variability requires more work on sensors that can be used with model aircraft platforms.
The objectives of this research were to study the association in bread wheat between spectral reflectance indices (SRIs) and grain yield, estimate their heritability, and correlated response to selection (CR) for grain yield estimated from SRIs under reduced irrigation conditions. Reflectance was measured at three different growth stages (booting, heading and grainfilling) and five SRIs were calculated, namely normalized difference vegetation index (NDVI), simple ratio (SR), water index (WI), normalized water index-1 (NWI-1), and normalized water index-2 (NWI-2). Three field experiments were conducted (each with 30 advanced lines) in three different years. Two reduced irrigation environments were created: (1) one-irrigation level (pre-planting), and (2) two-irrigation level (pre-planting and at booting stage), both representing levels of reduced moisture. Maximum yield levels in the experimental zone were generally obtained with 4–6 irrigations. Genotypic variations for all SRIs were significant. Three NIR (near infrared radiation) based indices (WI, NWI-1, and NWI-2) gave the highest level of association (both phenotypic and genotypic) with grain yield under both reduced irrigation environments. Use of the mean SRI values averaged over growth stages and the progressive integration of SRIs from booting to grainfilling increased the capacity to explain variation among genotypes for yield under these reduced irrigation conditions. A higher level of broad-sense heritability was found with the two-irrigation environment (0.80) than with the one-irrigation environment (0.63). Overall, 50% to 75% of the 12.5% highest yielding genotypes, and 50% to 87% of the 25% highest yielding genotypes were selected when the NWI-2 index was applied as an indirect selection tool. Strong genetic correlations, moderate to high heritability, a correlated response for grain yield close to direct selection for grain yield, and a very high efficiency of selecting superior genotypes indicate the potential of using these three SRIs in breeding programs for selecting increased genetic gains in grain yield under reduced irrigation conditions.
The photochemical reflectance index (PRI), derived from narrow-band reflectance at 531 and 570 nm, was explored as an indicator
of photosynthetic radiation use efficiency for 20 species representing three functional types: annual, deciduous perennial,
and evergreen perennial. Across species, top-canopy leaves in full sun at midday exhibited a strong correlation between PRI
and ΔF/Fm′, a fluorescence-based index of photosystem II (PSII) photochemical efficiency. PRI was also significantly correlated
with both net CO2 uptake and radiation use efficiency measured by gas exchange. When species were examined by functional type, evergreens exhibited
significantly reduced midday photosynthetic rates relative to annual and deciduous species. This midday reduction was associated
with reduced radiation use efficiency, detectable as reduced net CO2 uptake, PRI, and ΔF/Fm′ values, and increased levels of the photoprotective xanthophyll cycle pigment zeaxanthin. For each
functional type, nutrient deficiency led to reductions in both PRI and ΔF/Fm′ relative to fertilized controls. Laboratory
experiments exposing leaves to diurnal courses of radiation and simulated midday stomatal closure demonstrated that PRI changed
rapidly with both irradiance and leaf physiological state. In these studies, PRI was closely correlated with both ΔF/Fm' and
radiation use efficiency determined from gas exchange at all but the lowest light levels. Examination of the difference spectra
upon exposure to increasing light levels revealed that the 531 nm Δ reflectance signal was composed of two spectral components.
At low irradiance, this signal was dominated by a 545-nm component, which was not closely related to radiation use efficiency.
At progressively higher light levels above 100 μmol m−2 s−1, the 531-nm signal was increasingly dominated by a 526-nm component, which was correlated with light use efficiency and with
the conversion of the xanthophyll pigment violaxanthin to antheraxanthin and zeaxanthin. Further consideration of the two
components composing the 531-nm signal could lead to an index of photosynthetic function applicable over a wide range of illumination.
The results of this study support the use of PRI as an interspecific index of photosynthetic radiation use efficiency for
leaves and canopies in full sun, but not across wide ranges in illumination from deep shade to full sun. The discovery of
a consistent relationship between PRI and photosynthetic radiation use efficiency for top-canopy leaves across species, functional
types, and nutrient treatments suggests that relative photosynthetic rates could be derived with the “view from above” provided
by remote reflectance measurements if issues of canopy and stand structure can be resolved.
The development of a viable lignocellulosic ethanol industry requires multiple improvements in the process of converting biomass
to ethanol. A key step is the improvement of the plants that are to be used as biomass feedstocks. To facilitate the identification
and evaluation of feedstock plants, it would be useful to have a method to screen large numbers of individual plants for enhanced
digestibility in response to combinations of specific pretreatments and enzymes. This paper describes a high-throughput digestibility
platform (HTDP) for screening collections of germplasm for improved digestibility, which was developed under the auspices
of the Department of Energy-Great Lakes Bioenergy Research Center (DOE-GLBRC). A key component of this platform is a custom-designed
workstation that can grind and dispense 1–5mg quantities of more than 250 different plant tissue samples in 16h. The other
steps in the processing (pretreatment, enzyme digestion, and sugar analysis) have also been largely automated and require
36h. The process is adaptable to diverse acidic and basic, low-temperature pretreatments. Total throughput of the HTDP is
972 independent biomass samples per week. Validation of the platform was performed on brown midrib mutants of maize, which are known to have enhanced digestibility. Additional validation was performed by screening approximately
1,200 Arabidopsis mutant lines with T-DNA insertions in genes known or suspected to be involved in cell wall biosynthesis. Several lines showed
highly significant (p < 0.01) increases in glucose and xylose release (20–40% above the mean). The platform should be useful for screening populations
of plants to identify superior germplasm for lignocellulosic ethanol applications and also for screening populations of mutant
model plants to identify specific genes affecting digestibility.
Two critical limitations for using current satellite sensors in real-time crop management are the lack of imagery with optimum spatial and spectral resolutions and an unfavorable revisit time for most crop stress-detection applications. Alternatives based on manned airborne platforms are lacking due to their high operational costs. A fundamental requirement for providing useful remote sensing products in agriculture is the capacity to combine high spatial resolution and quick turnaround times. Remote sensing sensors placed on unmanned aerial vehicles (UAVs) could fill this gap, providing low-cost approaches to meet the critical requirements of spatial, spectral, and temporal resolutions. This paper demonstrates the ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors. During summer of 2007, the platform was flown over agricultural fields, obtaining thermal imagery in the 7.5-13-mum region (40-cm resolution) and narrowband multispectral imagery in the 400-800-nm spectral region (20-cm resolution). Surface reflectance and temperature imagery were obtained, after atmospheric corrections with MODTRAN. Biophysical parameters were estimated using vegetation indices, namely, normalized difference vegetation index, transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index, and photochemical reflectance index (PRI), coupled with SAILH and FLIGHT models. As a result, the image products of leaf area index, chlorophyll content ( C <sub>ab</sub>), and water stress detection from PRI index and canopy temperature were produced and successfully validated. This paper demonstrates that results obtained with a low-cost UAV system for agricultural applications yielded comparable estimations, if not better, than those obtained by traditional manned airborne sensors.
The normalized difference vegetation index (NDVI) has been widely used for remote sensing of vegetation for many years. This index uses radiances or reflectances from a red channel around 0.66 μm and a near-IR channel around 0.86 μm. The red channel is located in the strong chlorophyll absorption region, while the near-IR channel is located in the high reflectance plateau of vegetation canopies. The two channels sense very different depths through vegetation canopies. In this article, another index, namely, the normalized difference water index (NDWI), is proposed for remote sensing of vegetation liquid water from space. NDWI is defined as , where ϱ represents the radiance in reflectance units. Both the 0.86-μm and the 1.24-μm channels are located in the high reflectance plateau of vegetation canopies. They sense similar depths through vegetation canopies. Absorption by vegetation liquid water near 0.86 μm is negligible. Weak liquid absorption at 1.24 μm is present. Canopy scattering enhances the water absorption. As a result, NDWI is sensitive to changes in liquid water content of vegetation canopies. Atmospheric aerosol scattering effects in the 0.86–1.24 μm region are weak. NDWI is less sensitive to atmospheric effects than NDVI. NDWI does not remove completely the background soil reflectance effects, similar to NDVI. Because the information about vegetation canopies contained in the 1.24-μm channel is very different from that contained in the red channel near 0.66 μm, NDWI should be considered as an independent vegetation index. It is complementary to, not a substitute for NDVI. Laboratory-measured reflectance spectra of stacked green leaves, and spectral imaging data acquired with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) over Jasper Ridge in California and the High Plains in northern Colorado, are used to demonstrate the usefulness of NDWI. Comparisons between NDWI and NDVI images are also given.
Recent advancements in low-cost remote sensing equipment and techniques have proven that crop parameter classification can be practical in agricultural situations. One problem still remaining is how to account for in-field variability of mixed ground-cover areas. This study focused on finding a non-invasive method of screening low ground-cover areas before assessing crop nitrogen (N) status. Four years of plot data were used where intense ground sampling was coupled with nadir-viewing remotely sensed data from a ground-based remote sensing system. Data were collected over irrigated corn (Zea mays L.) with both light and dark soil backgrounds to develop a relationship between leaf area index (LAI) and remotely sensed data. An index based on subtracting the red from the green reflectance (green - red) was shown to be strongly related to LAI. A concurrent assessment of the N reflectance index (NRI) over the four-year study indicated that the corn N status classification improved with increasing LAI. Using the (green - red) index and a screening value corresponding to an LAI of 2.5, the data from the four-year study were screened and the NRI was reassessed. The initial screening removed all bare-soil and many of the measurements collected before the VI2 growth stage. In this example, 94.0% of the data associated with LAI below 2.5 was correctly filtered out, and 95.4% of the data collected over areas with LAI at or above 2.5 correctly passed the filter. This method of filtering was shown to be highly effective in screening low ground-cover and bare-soil areas before classification. Future incorporation of this technique into on-the-go filtering algorithms could improve crop parameter classification from remotely sensed measurements in early-season and mixed vegetative cover situations.
To exploit the wealth of gene sequence information provided by the 'genomics revolution' and mine agricultural germplasm for genetic diversity, high resolution, high throughput technologies in plant physiology are required for bridging the gap between genotype and phenotype. This special issue is dedicated to plant phenomics approaches to provide the quantitative phenotyping needed to elucidate the genetic bases for agricultural traits, and to screen germplasm for genetic variation in form, function and performance. These new techniques will enable the discovery of mechanisms and adaptations of plant responses to the environment.
Within just the past few years, next-generation sequencing (NGS) platforms have reduced the cost of DNA sequencing by several orders of magnitude. With these lower costs, DNA sequencing is increasingly powerful as a single-data format onto which a broad range of biological processes can be projected for high-throughput molecular profiling. Although significant challenges remain, translating these powerful technologies into useful laboratory tests has great potential. This chapter provides a comparative analysis of currently available NGS techniques, including those of Illumina, 454/Roche, Applied Biosystems, and Helicos BioSciences, and addresses emerging techniques, such as Pacific Biosciences single-molecule sequencing and nanopore sequencing. Furthermore, very recent electronic sequencing from Ion Torrent is elaborated. The chapter discusses advantages and disadvantages of individual platforms, and those common to all techniques, including template preparation and genome enrichment.
The development of new soil and plant sensors continues to be an area of emphasis in precision agriculture. Sensing technology can be used in plant breeding to facilitate selection of plant varieties with superior traits and in management to optimize production inputs for crop protection and growth. One example is the use of spectral sensors for site-specific fertility management applications when they are integrated with GPS and rate controllers. In spite of the progress made in sensor development, there is still a need to investigate methods for field deployment of these sensors with the use of ground systems. In this paper, we analyze the benefits and limitations of a ground-based proximal sensing platform retrofitted with plant height, canopy temperature and reflectance sensors. The platform consisted of a high-clearance sprayer modified with a front boom for sensor mounting. Other instrumentation included a GPS-RTK for positioning data and sensor data acquisition system. Machine field capacity, timeliness and other factors are analyzed in the context of field operation, and a general description of the vehicle operational characteristics are given from the mechanical perspective.