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Segmentation of the storage root image (see Supporting Information Code S1 and Dataset S1 and S2). After binarizing the image, we used connected component labeling to identify each root on the black background. We applied the medial axis transform to each storage root and derived length and diameter at each location of the medial axis. The units of x‐ and y‐ax are pixel
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Societal Impact Statement: Micronutrient deficiency or “hidden hunger” is estimated to affect two billion people worldwide and increasing the micronutrient concentration of food could play an important role in tackling this global challenge. Using a combination of imaging techniques and atomic absorption spectroscopy, we describe a link between roo...
Citations
... Image-based phenotyping of cassava storage roots reported so far is mainly restricted to individual roots, such as root lengths and widths 13 , while the aspects of the whole root crown have not been fully explored. Only root angle and crown diameter parameters, which could be retrieved from 2D photos, have been used to represent CRC data 12 . ...
Phenotypic analysis of cassava root crowns (CRCs) so far has been limited to visual inspection and very few measurements due to its laborious process in the field. Here, we developed a platform for acquiring 3D CRC models using close-range photogrammetry for phenotypic analysis. The state of the art is a low cost and easy to set up 3D acquisition requiring only a background sheet, a reference object and a camera, compatible with field experiments in remote areas. We tested different software with CRC samples, and Agisoft and Blender were the most suitable software for generating high-quality 3D models and data analysis, respectively. We optimized the workflow by testing different numbers of images for 3D reconstruction and found that a minimum of 25 images per CRC can provide high quality 3D models. Up to ten traits, including 3D crown volumes, 3D crown surface, root density, surface-to-volume ratio, root numbers, root angle, crown diameter, cylinder soil volume, CRC compactness and root length can be extracted providing novel parameters for studying cassava storage roots. We applied this platform to partial-inbred cassava populations and demonstrated that our platform provides reliable 3D CRC modelling for phenotypic analysis, analysis of genetic variances and supporting breeding selection.
... Root system architecture (RSA) greatly influences nutrient access, efficient water uptake, and plant tolerance to stress York et al., 2018;Mattupalli et al., 2019;Busener et al., 2020;McKay Fletcher et al., 2020;Seo et al., 2020). Increased efforts in breeding for desirable RSA traits can drive a breakthrough in crop productivity and resource efficiency (Lynch, 2007). ...
... Overall, image-based RSA phenotyping has many applications, such as linking RSA traits to micronutrient concentration and heritability (Busener et al., 2020;McKay Fletcher et al., 2020), the effect of dwarf genes in seedling roots (Wojciechowski et al., 2009), changes in the root crown in response to disease (Corona-Lopez et al., 2019;Mattupalli et al., 2019), to investigate root plasticity , genetically driven root architecture differences (Jiang et al., 2019), and QTL mapping of regions controlling RSA (Topp et al., 2013). Most of the studies cited above use a combination of tools for RSA trait extraction (DIRT; Das et al., 2015), RhizoVision , RSA-GiA (Galkovskyi et al., 2012;Topp et al., 2013), or Rootscape ) followed by statistical analysis (variations of ANOVA, three-parameter logistic function, PCA) or linear regression to test if the observed traits relate to environmental or genetic data. ...
High throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors such as RGB cameras, hyperspectral sensors and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterise crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalising between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation and quantitative trait measurement. We emphasise the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.
... Considering the worldwide importance of cassava, especially within developing countries, yield improvements would help strengthen economic growth in these areas by shifting its cultivation from that of subsistence to a cash crop [7]. Two areas in which cassava improvement is needed are early bulking (EB) of roots [8,9] and higher biomass of aboveground material [10,11]. Early bulking genotypes would have two significant advantages: a shortening of crop duration and increased yield [12]. ...
Challenges in rapid prototyping are a major bottleneck for plant breeders trying to develop the needed cultivars to feed a growing world population. Remote sensing techniques, particularly LiDAR, have proven useful in the quick phenotyping of many characteristics across a number of popular crops. However, these techniques have not been demonstrated with cassava, a crop of global importance as both a source of starch as well as animal fodder. In this study, we demonstrate the applicability of using terrestrial LiDAR for the determination of cassava biomass through binned height estimations, total aboveground biomass and total leaf biomass. We also tested using single LiDAR scans versus multiple registered scans for estimation, all within a field setting. Our results show that while the binned height does not appear to be an effective method of aboveground phenotyping, terrestrial laser scanners can be a reliable tool in acquiring surface biomass data in cassava. Additionally, we found that using single scans versus multiple scans provides similarly accurate correlations in most cases, which will allow for the 3D phenotyping method to be conducted even more rapidly than expected.
Nutrient stress is a worldwide problem which may alter the biochemical, physiological, and molecular processes in all kinds of plants. In addition, such nutritional stress is the major cause of malnutrition in the developing and poor countries. Generally, plants require 17 macro and micro nutrients for the optimum growth, development, and yield. Moreover, some other additional mineral elements are very crucial for the survival of the plants under stress conditions or help the farmer to produce the quality products. The proper and timely management could reduce its impacts. The impact of nutrient stress depends on plant age, soil types, plant species, ecology, climatic conditions, and genome of it. Usually, morphological characteristics of the plants are considered the quick, valuable, accurate, and strong identification of nutritional deficiency of the specific nutrients. Biochar (BC) is a cheap potential source of Carbon (C) which not only improves health and fertility of soil but also improves the quality and productivity of crops both in normal and under stress conditions. Here we reviewed that BC is the source of various kind of elements such as C, H, N, P, K, Mg, Ca, S and some other nutrients that are key for healthy plant growth. Moreover, it improves the soil physico-chemical properties such soil porosity, surface area, CEC, soil hydrophobic capacity, soil aeration and soil surface oxidation which results into increase in soil nutrients availability and further their retention in the rhizosphere. In conclusion, all these properties of BC could help the plant to survive under the nutrients stress conditions.KeywordsNutrient stressBiocharEnvironmental factorsClimate changePlant growth
Searching for high yielding cassava cultivars
involves the measurements of cassava storage root morphology.
This can be accurately achieved by using three-dimensional root
skeletons extracting from three-dimensional images of cassava
storage roots. We proposed a spherical search approach to
effectively extract the three-dimensional root skeletons and a
point density approach to approximate the location of stem-root
junction. Our techniques were applied to three-dimensional
images with uniform distributed points derived from voxel
craving. The skeletal points are generated with equal distances
between any adjacent points and sorted by distances from the
root tip. Root segmentation is performed along with the root
skeletal extraction. Moreover, fragmented skeletons caused by
missing parts in three-dimensional reconstruction process are
reconnected in order to increase the accuracy of root trait
measurements especially the root count and root lengths.
Phenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R² ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R² = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability.
Interminable plant production with negligent management might lead to zinc (Zn) depletion in soils, which could induce a Zn deficit in staple foods and cause human Zn malnutrition in many of the least-developed countries. Knowledge of the Zn pool and balance in soil-cassava systems is unclear and requires a detailed investigation. Herein, we examined Zn mass inputs (weathering and manure) and output (harvest crop) from 34 locations of cassava-grown soils in Thailand. The results revealed that the median available Zn concentration (0.47 mg kg−1) of the studied soils was below the critical Zn requirement for cassava production. The total Zn stock in the soils varied considerably from 9.0 to 633 kg Zn ha−1, only 1% of which was identified as the available pool, and the rest of the fraction was considered as the reserved pool. Strong linear relationships (R2 > 0.61–0.90) of total Zn concentration with clay, organic matter, and total Fe concentration suggested that phyllosilicate, organic matter, and Fe oxyhydroxides were the primary hosts for Zn in the soils. Belowground biomass (70% of the total biomass) was the most abundant biomass fraction, whereas 88% of the total Zn uptake accumulated in the aboveground biomass. The Zn output from the biomass harvest was 61–642 g Zn ha−1 yr−1, corresponding to only 0.10% of the total soil Zn stock. Most locations (91% of the studied sites) received Zn input solely from mineral weathering (0.014–0.975 g Zn ha−1 yr−1) without Zn fertilizer and manure additions, causing net Zn depletions (–642 to –61 g Zn ha−1 yr−1). A few locations had net Zn accumulations (+123 to + 1,971 g Zn ha−1 yr−1) caused by animal manure additions. Without the manure additions, the total Zn stock was estimated to decline by only 1% of the total current Zn stock within the next 10 years, whereas the manure additions would enhance the total Zn stock by 0.26–7.68%. This study highlighted that the Zn input from weathering is meager for cassava production. Combined Zn fertilizer and organic manure are urgently required to resupply and replenish readily available Zn pools and to promote the total Zn stock for sustaining long-term crop production.
