A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping

Gill, Taqdeer; Gill, Simranveer K.; Saini, Dinesh Kumar; Chopra, Yuvraj; Koff, Jason P. de; Sandhu, Karansher S.

doi:10.1007/s43657-022-00048-z

Cited by 80 publications

(47 citation statements)

References 220 publications

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“…The DL models used for Phenomics mainly include multilayer perceptron, generative antagonism network, convolutional neural network (CNN), and recurrent neural network. CNN has great advantages in image analysis, and different CNN networks are used for different plants ( Gill et al, 2022 ). CNNs in deep learning demonstrate powerful feature extraction capabilities for images and are widely used in image-based agricultural computer vision tasks.…”

Section: Introductionmentioning

confidence: 99%

Exploring Soybean Flower and Pod Variation Patterns During Reproductive Period Based on Fusion Deep Learning

et al. 2022

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The soybean flower and the pod drop are important factors in soybean yield, and the use of computer vision techniques to obtain the phenotypes of flowers and pods in bulk, as well as in a quick and accurate manner, is a key aspect of the study of the soybean flower and pod drop rate (PDR). This paper compared a variety of deep learning algorithms for identifying and counting soybean flowers and pods, and found that the Faster R-CNN model had the best performance. Furthermore, the Faster R-CNN model was further improved and optimized based on the characteristics of soybean flowers and pods. The accuracy of the final model for identifying flowers and pods was increased to 94.36 and 91%, respectively. Afterward, a fusion model for soybean flower and pod recognition and counting was proposed based on the Faster R-CNN model, where the coefficient of determinationR2 between counts of soybean flowers and pods by the fusion model and manual counts reached 0.965 and 0.98, respectively. The above results show that the fusion model is a robust recognition and counting algorithm that can reduce labor intensity and improve efficiency. Its application will greatly facilitate the study of the variable patterns of soybean flowers and pods during the reproductive period. Finally, based on the fusion model, we explored the variable patterns of soybean flowers and pods during the reproductive period, the spatial distribution patterns of soybean flowers and pods, and soybean flower and pod drop patterns.

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Section: Introductionmentioning

confidence: 99%

Exploring Soybean Flower and Pod Variation Patterns During Reproductive Period Based on Fusion Deep Learning

et al. 2022

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“…Thermal imaging sensors measure re ection from infrared regions to determine transpiration rate and canopy temperature, potentially linked to different biotic stresses in plants. These imaging tools have been used to detect biotic and abiotic stresses, plant water status, and maturity in soybean (Gill et al 2022). Sandmann et al (2018) used thermography imaging to detect biotic stresses in lettuce; the same technique could be used to differentiate and detect the occurrence of various biotic stresses in soybean.…”

Section: Thermal Imagingmentioning

confidence: 99%

“…Photosystem II's uorescence emissions determine phytochemistry changes in plants using uorescence imaging (Gill et al 2022). These uorescnent sensors provide information on chlorophyll content, photosynthetic rate, and various physiological processes, which are linked indirectly to different biotic stresses in plants.…”

Section: Fluorescence Imagingmentioning

confidence: 99%

“…X-ray CT has also been used to study quality traits and plant morphology. Various other tools are available to study different biotic stresses in crop plants, including light detection and ranging, ight time, positron emission tomography, radio detection and ranging, sound detection and ranging, and magnetic resonance imaging (see Arya et al 2022, Gill et al 2022).…”

Section: X-ray Computed Tomographymentioning

confidence: 99%

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Multi-omics assisted breeding for biotic stress resistance in soybean: challenges and opportunities

Bisht

Saini

Kaur

et al. 2022

Preprint

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Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, and fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker trait associations involved in regulating biotic stress responses of soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We recommend using ‘integrated omics’ to understand how soybean responds to different biotic stresses. We discuss the potential challenges of integrating multi-omics for functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.

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“…Owing to its wide distribution and climate variability, wheat is affected by various biotic (yellow, brown, and stem rusts, fusarium head blight, tan spot, and several other diseases, insects, and nematodes) and abiotic stresses (drought, heat, salinity, water-logging, pre-harvest sprouting, and mineral toxicity, among others) 6 . Breeding climate-resilient wheat cultivars is the best approach to assisting wheat in surviving abiotic stresses, which could be facilitated by mapping the genomic regions involved, marker-assisted breeding, and other advanced approaches such as genome editing, genomic-assisted breeding (involving the use of high-throughput genotyping and phenotyping systems), and haplotype-based breeding 7,8,4,5,9 .…”

Section: Introductionmentioning

confidence: 99%

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

Tanin

Saini

Sandhu

et al. 2022

Preprint

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In wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress, heat stress, salinity stress, water-logging stress, pre-harvest sprouting, and aluminium stress which predicted a total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent and stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six MQTLs out of the 132 physically anchored MQTLs were also verified with genome-wide association studies. Around 43% of MQTLs had genetic and physical confidence intervals of less than 1 cM and 5 Mb, respectively. Consequently, 539 genes were identified in some selected MQTLs providing tolerance to 5 or all 6 abiotic stresses. Comparative analysis of genes underlying MQTLs with four RNA-seq based transcriptomic datasets unravelled a total of 191 differentially expressed genes which also included at least 11 most promising candidate genes common among different datasets. The promoter analysis showed that the promoters of these genes include many stress responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, and WUN-motif among others. Further, some MQTLs also overlapped with as many as 34 known abiotic stress tolerance genes. In addition, numerous ortho-MQTLs among the wheat, maize, and rice genomes were discovered. These findings could help with fine mapping and gene cloning, as well as marker-assisted breeding for multiple abiotic stress tolerances in wheat.

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A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping

Cited by 80 publications

References 220 publications

Exploring Soybean Flower and Pod Variation Patterns During Reproductive Period Based on Fusion Deep Learning

Exploring Soybean Flower and Pod Variation Patterns During Reproductive Period Based on Fusion Deep Learning

Multi-omics assisted breeding for biotic stress resistance in soybean: challenges and opportunities

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

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