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Sensing Approaches for Precision Agriculture
Abstract
Many endeavours in precision agriculture use some kind of sensor to gain relatively inexpensive information on the spatial and temporal variation in crops, soil, weeds, diseases, and so on. However, information about sensors is scattered throughout the literature. This text fills an important niche by bringing together information on a wide range of sensors that are used in precision agriculture in one book. Included are sensors that are well-established and regularly used in commercial precision agriculture as well as those that are currently being developed and researched. The book contains review chapters, case study chapters and chapters that include both review and case study sections. The full range of sensors used in precision agriculture is considered in the review chapters: (2) Satellite Remote Sensing for Precision Agriculture, (3) Sensing Crop Geometry and Structure, (4) Soil Sensing, (5) Sensing with Wireless Sensor Networks, (6) Sensing for Health, Vigour and Disease Detection in Row and Grain Crops, (7) On-Combine Sensing Techniques in Arable Crops, (8) Sensing in Precision Horticulture, (9) Sensing from Unmanned Aerial Vehicles, and (10) Sensing for Weed Detection. These chapters provide a logical and thorough review of the types of sensors that have been used to observe different phenomena within precision agriculture as well as the delivery platforms that have been used for sensing. Readers are provided with a rapid overview of the sensing solutions currently adopted and the trends in research towards developing new applications. In addition, the pros and cons of particular sensing approaches are considered, the standard best practices for using such sensors are discussed, and in some cases indications of current relative costs are given. The chapters with case studies: 4) Soil Sensing, (10) Sensing for Weed Detection, (11) Applications of Sensing to Precision Irrigation, (12) Applications of Optical Sensing of Crop Health and Vigour, and (13) Applications of Sensing for Disease Detection give detailed examples of some typical and cutting-edge applications of sensors in precision agriculture and the kinds of insights that the sensors used can provide to the sub-fields of precision agriculture. The book ends with an evaluation of potential future directions in sensor research for precision agriculture, which sensors show most promise for certain applications and the areas where increased research emphasis should be applied. The text provides sufficient detail to act as a handbook for practitioners trying to determine the best sensing approaches to use in a given situation. The target audiences of this book are upper-level undergraduate and graduate students, new professionals, scientists and practitioners of precision agriculture, and agricultural engineers. The book could be used in general agriculture and precision agriculture courses and also in courses on environmental monitoring and policy making at
universities.
Provo, UT, USA Ruth Kerry
Lleida, Catalunya, Spain Àlex Escolà
... Standard approaches to developing nutrient MZs have used dense sensed data related to key soil properties (Adamchuk et al., 2004;Fridgen et al., 2004;Hummel et al., 1996;Triantifilis et al., 2012), but regression-based calibrations of sensed data have also been used to provide more detailed contour maps of soil variables (Kuang & Mouazen, 2012;Triantifilis et al. 2009;. A thorough review of soil sensing approaches for PA showed that electrical conductivity, gamma-radiometry, near infrared (NIR) spectroscopy, X-ray fluorescence (XRF) machines and high-resolution hyper-and multi-spectral imagery from unmanned aerial vehicles (UAVs) are commonly used to provide dense information on key soil properties, particularly in the topsoil (Adamchuk et al., 2021). In addition, visible (VIS) and short wave infra-red (SWIR) wavelengths have been used to gain insight into the variation in soil properties Zhao et al., 2018). ...
... The RMSEs from sensed data used to map and predict soil property values with relatively few calibration data are mentioned here for comparison with the results from the current study. Case Study 4.1 in Adamchuk et al. (2021) produced RMSEs of 0.38-0.63 for pH, 1.93-2.75% for clay, 6.39-8.74% ...
... for organic matter with 10-46 samples. The RMSEs of 0.3-0.7% for OM and 2-4% for clay were obtained for NIR spectroscopy with 30-40 calibration samples (Case study 4.3, Adamchuk et al., 2021). Case study 4.2 in Adamchuk et al. (2021) also showed RMSEs of 0.16-0.18 ...
Sensed and soil sample data are used in two approaches for mapping soil properties in precision agriculture: management zone (MZs) and contour maps. This is the second paper in a two-part series that focuses on contour maps. Detailed and accurate contour maps of soil properties for precision agriculture are often costly to produce because of the large sampling effort required. Such maps or those of sensed ancillary data are often simplified to represent MZs. This research investigated the accuracy of detailed maps of soil properties produced inexpensively from sensed data by transforming them to z-scores. The z-scores of ancillary values are then transformed to values of soil variables using the mean and standard deviation of a small soil data set. The errors from this mapping approach are examined with historic soil data from three field sites with different scales of spatial variation in the United Kingdom. Errors from the conversion of z-scores of sensed data to soil variable ranges are compared with those from MZ averages (Paper I in this series). For soil properties with a moderate relation to ancillary data, the errors related to the z-score conversion were small irrespective of sample size. The root mean squared errors associated with the MZ mean rather than values from the digital map were generally smaller except when sample size was very small. The results suggest that when the scale of variation is small and more samples are required to define MZs, calibrating z-scores of sensed ancillary data may provide better MZ averages than sampling on a grid; it also provides a detailed map of spatial variation within the field. The z-score conversion approach is less sensitive to sample size and captures small features of the variation compared to the standard 100 m grid sampling to determine MZ averages.
... Farmers are concerned that the economic benefits of adoption may not offset the investment. However, up to now, limited studies have analyzed the economic returns of using UAVs in precision agriculture (Andujar et al. 2019, Späti et al. 2021, and more empirical analysis should be conducted in the future to estimate the economic benefits of using UAVs (Kerry et al. 2021). Secondly, a relatively large arable land size (≥ 2 ha) and high-value crops producing on the farm. ...
In China, unmanned aerial vehicles (UAVs) are increasingly used for broadcast application of agricultural inputs such as pesticides, fertilizers, and seeds. UAVs have the potential for site-specific precision agriculture, facilitating precise management of fertilization, plant protection, and irrigation to reduce environmental footprint of farming. There has been research on the use of UAVs in agriculture , but less is known about UAV-based precision agriculture, particularly pattern management. To close these research gaps, this paper conducted structured in-depth interviews with 18 experts from various fields related to UAVs in Chinese agriculture to study the status quo, drivers, and barriers of adopting UAVs, focusing on UAV-based precision agriculture, particularly pattern management. The results show that the adoption of UAVs in China is influenced by farmers' production characteristics, farmers' perceptions about UAVs, and social factors. UAV-based precision agriculture is at the initial stage in China, and the promotion of this approach still needs to overcome technical barriers such as improving the accuracy of crop measurements, developing real-time UAV positioning systems, and enhancing the response time of variable-rate spraying systems, as well as socioeconomic barriers like farmers' limited UAV-related knowledge, small farm sizes, and lack of technical assistance.
... To address these uncertainties, canopy monitoring of super-intensive orchards is becoming increasingly important to ensure consistent quality and meet short-and medium-term production and sustainability targets (Arquero, Jarvis-Shean., 2017). The scientific literature has demonstrated the suitability of emerging technologies that rely heavily on sensors for orchard canopy monitoring (Zude-Sasse et al., 2021). Remote sensing, complementing ground-based sensors, has been widely used to monitor crop growth and estimate quality and yield from local to global scales (Sun et al., 2017;Barajas et al., 2020;Kasimati et al., 2021). ...
The use of super-intensive orchards is a growing trend in fruit production. The present study aims to improve management of these cropping systems by focusing on how agronomic decisions impact orchard dynamics in the short to medium term and by providing a decision-support approach based on stable temporal patterns from previous seasons. A multitemporal study using remote sensing and LiDAR was conducted in a commercial almond orchard over four growing seasons (2019-2022) to determine the optimal timing of image acquisition for variable pre-harvest treatments. A model-based clustering (mclust) was applied to optimal Sentinel-2 NDVI maps and apparent soil electrical conductivity (ECa) data, interpolated to the pixel centroids of Sentinel-2 image grids, to delineate potential management zones (PMZs). The leafiness-LiDAR index (LLI), a leaf area index (LAI) estimator, was obtained as ground truth after summer pruning and before harvesting, showing a significant influence of fertigation and pruning on the LAI, with summer pruning particularly influencing orchard dynamics. The optimal time for NDVI mapping was found to be two months after summer pruning in productive years and two weeks after in unproductive years. The delineated PMZs were consistent across seasons and corresponded to significant LAI differences. This method could contribute to improving resource management and sustainability in super-intensive commercial orchards.
... While handheld devices are not particularly expensive, gathering data on several variables at once on a dense grid with about 100 locations can take a few hours. Within precision agriculture, various sensing approaches have been embraced [38]. Sensors are generally more expensive than handheld devices, but far more dense datasets can be collected in a fraction of the time needed for a dense survey with handheld devices. ...
Turfgrass irrigation consumes a large amount of the scarce freshwater in arid/semi-arid regions. Approximately 50% of this irrigation water is wasted. It has been suggested that determining patterns of spatial variability in soil moisture to modify applications with valve-in-head sprinkler technology can greatly reduce waste. Variable rate irrigation (VRI) studies in traditional agricultural settings have shown that VRI zones do not stay static temporally and need to be frequently redetermined. Electrical conductivity (ECa) data from Geonics EM38 surveys and data from Red, Green, Blue (RGB) and Thermal Infra-Red (Th.IR) drone surveys are less time-consuming and therefore expensive to collect than a dense field survey of soil moisture and grass health to produce accurate geostatistical maps. Drone flights and ECa surveys are compared here for their ability to accurately estimate spatial patterns of soil volumetric water content (VWC) using simple linear regression and z-score transformations for prediction—non-geostatistical approaches that require less data. Overall, ECa readings collected in the horizontal mode were the most consistent at capturing spatial patterns in soil moisture. Predictions from regression produced lower root mean squared errors (RMSEs) for the larger datasets. However, z-score transformation produced lower RMSEs when the sample number was very small and preserved the scale of values better than the regression approach. The results suggested that predictions from ECa and drone data were useful for capturing key features in soil moisture patterns for 2–3 weeks, suggesting that a periodic reassessment of zones is needed. Using ECa and drone data in an urban environment is more labor-intensive than in an agricultural field, so it is likely that automating periodic re-surveying of ECa data for zone definition would only be cost-effective for golf courses or high-income sports fields. Elsewhere, using static zones with variable rates applied to each zone may be more efficient.
... All these advances contribute to the implementation of intelligent agricultural operations [24]. With the increasing adoption of these cutting-edge technologies in agriculture, the ability to characterize spatial variability and address the challenges that impede crop growth is becoming crucial to the success of PA [25,26]. ...
Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies.
... This survey was carried out after some variable-rate liming and fertilizer applications, which would reduce the between-zone variation. Case study 4.2 of Adamchuk et al. (2021) showed that such management effects should be considered when investigating spatial patterns of soil properties where variable-rate management has been used. For the 1994 survey data, there is no significant difference between the zones for any of the variables at p < 0.05, but Mg shows significant differences at p < 0.1. ...
Sensed and soil sample data are used in two main approaches for mapping soil properties in precision agriculture: management zones (MZs) and contour maps. This is the first of two papers that explores maps of MZs. Management zones based on variation in sensed data that are related to the more permanent soil properties assume that the zones are multi-purpose. Soil properties are then often sampled on a grid to provide the average values of each property per zone. This paper examines the plausibility of this approach by examining how the number of samples taken on a grid and the application of kriging affect mean soil property values for MZs. The suitability of MZs based on ancillary data for managing several agronomically important properties simultaneously is also considered. These concepts are examined with historic soil data from four field sites in southern UK with different scales of spatial variation. Results showed that when the grid sampling interval is large, there is less difference in the means of properties between MZs, but kriging the soil data increased the differences between zones when the sampling interval was large and sample small. Sensed data are used increasingly to aid the identification of MZs, but these could not be considered multi-purpose at all sites. The MZs produced were most useful for phosphorus (P), pH and volumetric water content (VWC) at the Wallingford site and useful for most properties at the Clays and Y215 sites. For the latter site this was true only when the most dense data were used to calculate MZ averages. The results show that sampling interval for MZ averages should relate to the scale of variation or the size of the MZs at a site. The sampling density could be based on the variogram range of ancillary data. This research suggests that there should be 6–8 samples per zone to obtain accurate averages of soil properties. Nutrient data for more than one year were examined at two sites and showed that patterns remained consistent in the short term unless variable-rate management was used, but also the range of values changed in the short term.
This study proposes an automated data-labeling model that combines a pseudo-labeling algorithm with waveform segmentation based on Long Short-Term Memory (LSTM) to effectively label time-series data in smart agriculture. This model aims to address the inefficiency of manual labeling for large-scale data generated by agricultural systems, enhancing the performance and scalability of predictive models. Our proposed method leverages key features of time-series data to automatically generate labels for new data, thereby improving model accuracy and streamlining data processing. By automating the labeling process, we reduce dependence on manual labeling, which is often labor-intensive and prone to errors in large datasets. This approach enables the efficient preparation of labeled data for applications such as anomaly detection, pattern recognition, and predictive modeling in smart agriculture. Experimental results demonstrate that the automated labeling model achieves 89% accuracy in agricultural environments and reduces data processing time by 30%. Future research will focus on extending the model’s applicability to diverse agricultural settings, enhancing generalization performance, and improving real-time processing capabilities, thereby advancing intelligent and sustainable smart agriculture systems.
This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production efficiency and sustainability. This is essential in the context of varying climatic conditions that affect the availability of resources for agriculture. The integration of tools such as IoT and sensor networks can allow farmers to obtain real-time data on their crops, assessing key health factors, such as soil conditions, plant water status, presence of pests, and environmental factors, among others, which can finally result in data-based decision-making to optimize irrigation, fertilization, and pest control. Also, this can be enhanced by incorporating tools such as drones and unmanned aerial vehicles (UAVs), which can increase monitoring capabilities through comprehensive field surveys and high-precision crop growth tracking. On the other hand, big data analytics and AI are crucial in analyzing extensive datasets to uncover patterns and trends and provide valuable insights for improving agricultural practices. This paper highlights the key technological advancements and applications in smart crop management, addressing challenges and barriers to the global adoption of these current and new types of technologies and emphasizing the need for ongoing research and collaboration to achieve sustainable and efficient crop production.
This thesis focuses on the detection (counting) of fruits and estimation of their size and weight in apple orchards through the application of computer vision techniques. This work seeks to provide fruit growers with advanced tools and methodologies to help them make accurate harvest yield predictions. Counting (quantifying) and locating fruits represent previous steps to achieve these predictions. By knowing this information, fruit growers can schedule in advance the required resources for harvest and post-harvest (labor, transportation, storage), design sales strategies and, ultimately, optimize the profitability of their farms. In addition, it is also essential to control fruit quality parameters such as size and weight, which have a great influence on the market price and decision making for canopy management. On the other hand, today, counting, locating and measuring the size of fruits are repetitive tasks that require trained labor and that can be affected by fatigue and the subjective criterion of workers. Therefore, the manual execution of these tasks in fruit orchards of several hectares is not feasible. These reasons largely explain the current need to develop automatic tools that allow accurate in-field fruit detection and sizing.
The main objective of this thesis is to explore the capacity of RGB-D sensors to estimate the size and weight of fruits in apple trees. The main body of this work is made up of four articles that deal in detail with various stages throughout the process: i) a review of the state of the art on fruit sizing using artificial intelligence techniques and its challenges in field conditions; ii) the development of software tools for data acquisition in fruit orchards; iii) the development of algorithms for estimating fruit size and weight using allometric models; and iv) an experimental field evaluation of the implemented algorithms, applying combinations of fruit sizing methods and allometric models for weight prediction. The results obtained presented errors (MAPE) of less than 5 % in the estimation of the size of non-occluded apples and less than 5.1% for the prediction of their weight. These results open the possibility of using affordable RGB-D cameras in the short term for real-time characterization of fruit plantations. Furthermore, as a result of this thesis, a set of open access software tools has been made available to the public for in-field fruit detection and estimate their size and weight. In conclusion, the thesis represents a contribution towards the development of affordable tools to facilitate decision-making and help optimize fruit orchard management.
[http://hdl.handle.net/10803/690455]
Vertical greenery systems (VGS) have been proposed as a nature-based solution to mitigate the adverse effects of urban heat islands and climate change in cities. However, large-scale VGS are costly and require ongoing maintenance, typically carried out manually through trial and error based on professional experience. Advanced management is essential for the sustainability of VGS due to its limited accessibility and associated costs. To address these challenges, we examined the use of remote sensing methods for outdoor VGS monitoring as a basis for a precision agriculture approach for VGS management and maintenance. This study presents the first ongoing monitoring of real-scale VGS using thermal, hyperspectral, and RGB vegetation indices. These indices were employed for the early detection of vegetation stress, focusing on two case studies exhibiting visible yellowing symptoms. Through the application of unsupervised classification techniques, stressed pixels were successfully detected 14–35 days before visual yellowing, achieving an accuracy of 0.85–0.91. Additionally, the thermal index provided valuable information regarding the spatial distribution of watering along the VGS. Stress maps based on noninvasive methods were demonstrated, forming the basis of a spatial decision support system capable of detecting issues related to plant vitality and VGS irrigation management.
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