Phenotyping Agronomic Traits of Peanuts using UAV-based Hyperspectral Imaging and Deep Learning

Bagherian, Kamand; Puhl, Rafael Bidese; Yin, Bo; Zhang, Qiong; Sanz‐Sáez, Álvaro; Chen, Charles; Dang, Phat

doi:10.13031/aim.202200814

Cited by 3 publications

(8 citation statements)

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“…Drought-tolerant peanut cultivars can exhibit high WUE or EUW. Peanut cultivars with high EUW do not show a yield advantage compared to high WUE under midseason drought (Zhang et al, 2022). The high EUW capacity of these peanut cultivars has not yet been associated with a more profound or complex root system, as evidenced in the common bean (White et al, 1990).…”

Section: Effective Use Of Water Due To More Complex Root Systemmentioning

confidence: 96%

“…Regarding remote sensing of peanut yield and yield components, Bagherian et al (2022) recently evaluated predicting biomass, pod count, and pod yield using UAV-based hyperspectral imaging and machine learning techniques for single peanut plants of an F 1 population in a mid-season drought experiment using rainout shelters. Eighteen days after the drought was found to result in the highest prediction accuracies for the three agronomic traits ( R 2 = 0.52–0.61).…”

Section: High-throughput Phenomics To Accelerate Data Collection In G...mentioning

confidence: 99%

“…Reduced leaf area expansion and lower photosynthesis per leaf area lead to a decline in canopy carbon assimilation that will reduce biomass accumulation and yield (Reddy et al, 2003). Plant traits that preserve soil moisture, such as highwater use efficiency (WUE) due to rapid stomatal closure, could increase drought tolerance (Devi et al, 2010;Devi and Sinclair, 2011;Shekoofa et al, 2015;Sinclair et al, 2017;Zhang et al, 2022). Contrarily, there are peanut cultivars that can maintain adequate plant water status and escape drought by collecting more water due to a more complex or deep root system (Rowland et al, 2012a;TABLE 3 (Continued) Genotypes exhibiting heat stress tolerance along with its responsive trait.…”

Section: Drought Stressmentioning

confidence: 99%

“…Frontiers in Genetics frontiersin.org Zhang, et al, 2022). Utilization of genomic and genetic resources for developing peanuts for harsh environment conditions are illustrated in Figure 1.…”

Section: Abiotic Type Tolerant Germplam Evaluated Trait Figurementioning

confidence: 99%

“…Several such sets are reported for efficient utilization of genetic resources in peanut breeding and genetics (Ding et al, 2022;Dwivedi et al, 2008;Holbrook and Stalker, 2003;Holbrook and Dong, 2005;Upadhyaya et al, 2002a and;2002b). The U.S. peanut mini-core collection has been effectively used for the identification of interesting alleles and traits for breeding programs for traits related to drought tolerance (Wang H, et al, 2016;Wang M. L. et al, 2016;Zhang et al, 2019;Li et al, 2022;Patel et al, 2022;Zhang et al, 2022). End-of-season drought tolerance was reported in 15 accessions after evaluating ICRISAT peanut mini core collection and selected based on pod yield, SPAD, and SLA measurements (Upadhyaya, 2005).…”

Section: Cultivated Genepoolmentioning

confidence: 99%

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Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance

et al. 2023

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Climate change is significantly impacting agricultural production worldwide. Peanuts provide food and nutritional security to millions of people across the globe because of its high nutritive values. Drought and heat stress alone or in combination cause substantial yield losses to peanut production. The stress, in addition, adversely impact nutritional quality. Peanuts exposed to drought stress at reproductive stage are prone to aflatoxin contamination, which imposes a restriction on use of peanuts as health food and also adversely impact peanut trade. A comprehensive understanding of the impact of drought and heat stress at physiological and molecular levels may accelerate the development of stress tolerant productive peanut cultivars adapted to a given production system. Significant progress has been achieved towards the characterization of germplasm for drought and heat stress tolerance, unlocking the physiological and molecular basis of stress tolerance, identifying significant marker-trait associations as well major QTLs and candidate genes associated with drought tolerance, which after validation may be deployed to initiate marker-assisted breeding for abiotic stress adaptation in peanut. The proof of concept about the use of transgenic technology to add value to peanuts has been demonstrated. Advances in phenomics and artificial intelligence to accelerate the timely and cost-effective collection of phenotyping data in large germplasm/breeding populations have also been discussed. Greater focus is needed to accelerate research on heat stress tolerance in peanut. A suits of technological innovations are now available in the breeders toolbox to enhance productivity and nutritional quality of peanuts in harsh environments. A holistic breeding approach that considers drought and heat-tolerant traits to simultaneously address both stresses could be a successful strategy to produce climate-resilient peanut genotypes with improved nutritional quality.

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Section: Effective Use Of Water Due To More Complex Root Systemmentioning

confidence: 96%

Section: High-throughput Phenomics To Accelerate Data Collection In G...mentioning

confidence: 99%

Section: Drought Stressmentioning

confidence: 99%

Section: Abiotic Type Tolerant Germplam Evaluated Trait Figurementioning

confidence: 99%

Section: Cultivated Genepoolmentioning

confidence: 99%

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Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance

et al. 2023

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HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

Chang,

Chowdhry,

Song

et al. 2023

Preprint

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Collecting and analyzing hyperspectral imagery (HSI) of plant roots over time can enhance our understanding of their function, responses to environmental factors, turnover, and relationship with the rhizosphere. Current belowground red-green-blue (RGB) root imaging studies infer such functions from physical properties like root length, volume, and surface area. HSI provides a more complete spectral perspective of plants by capturing a high-resolution spectral signature of plant parts, which have extended studies beyond physical properties to include physiological properties, chemical composition, and phytopathology. Understanding crop plants’ physical, physiological, and chemical properties enables researchers to determine high-yielding, drought-resilient genotypes that can withstand climate changes and sustain future population needs. However, most HSI plant studies use cameras positioned above ground, and thus, similar belowground advances are urgently needed. One reason for the sparsity of belowground HSI studies is that root features often have limited distinguishing reflectance intensities compared to surrounding soil, potentially rendering conventional image analysis methods ineffective. In the field of machine learning (ML), there are currently no publicly available datasets containing the heavy correlation, highly textured background, and thin features characteristic of belowground root systems. Here we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species – peanut (Arachis hypogaea) and sweet corn (Zea mays). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. Additionally, we present baselines for root segmentation on this dataset and draw comparisons between methods that focus on spatial, spectral, and spatial-spectral features to predict the pixel-wise labels. Results demonstrate that combining HyperPRI’s hyperspectral and spatial information improves semantic segmentation of target objects.

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High-Throughput Canopy and Belowground Phenotyping of a Set of Peanut CSSLs Detects Lines with Increased Pod Weight and Foliar Disease Tolerance

et al. 2023

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We deployed field-based high-throughput phenotyping (HTP) techniques to acquire trait data for a subset of a peanut chromosome segment substitution line (CSSL) population. Sensors mounted on an unmanned aerial vehicle (UAV) were used to derive various vegetative indices as well as canopy temperatures. A combination of aerial imaging and manual scoring showed that CSSL 100, CSSL 84, CSSL 111, and CSSL 15 had remarkably low tomato spotted wilt virus (TSWV) incidence, a devastating disease in South Georgia, USA. The four lines also performed well under leaf spot pressure. The vegetative indices showed strong correlations of up to 0.94 with visual disease scores, indicating that aerial phenotyping is a reliable way of selecting under disease pressure. Since the yield components of peanut are below the soil surface, we deployed ground penetrating radar (GPR) technology to detect pods non-destructively. Moderate correlations of up to 0.5 between pod weight and data acquired from GPR signals were observed. Both the manually acquired pod data and GPR variables highlighted the three lines, CSSL 84, CSSL 100, and CSSL 111, as the best-performing lines, with pod weights comparable to the cultivated check Tifguard. Through the combined application of manual and HTP techniques, this study reinforces the premise that chromosome segments from peanut wild relatives may be a potential source of valuable agronomic traits.

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Phenotyping Agronomic Traits of Peanuts using UAV-based Hyperspectral Imaging and Deep Learning

Cited by 3 publications

References 0 publications

Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance

Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance

HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

High-Throughput Canopy and Belowground Phenotyping of a Set of Peanut CSSLs Detects Lines with Increased Pod Weight and Foliar Disease Tolerance

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