ArticlePlant phenomics (Washington, D.C.)2021
Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning.
Article in Plant phenomics (Washington, D.C.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A cytochrome P450 gene, GmSUR2Journal of integrative plant biology · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- Drought tolerance in soybean: genetics, metabolomics, remote sensing, and breeding for enhanced drought tolerance.Frontiers in plant science · 2026Review
- Multi-Scale Remote-Sensing Phenomics Integrated with Multi-Omics: Advances in Crop Drought-Heat Stress Tolerance Mechanisms and Perspectives for Climate-Smart Agriculture.Plants (Basel, Switzerland) · 2025Review
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- RGB imaging-based evaluation of waterlogging tolerance in cultivated and wild chrysanthemums.Plant phenomics (Washington, D.C.) · 2025Article
- Soybean genomics research community strategic plan: A vision for 2024-2028.The plant genome · 2024Article
- Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review.Plants (Basel, Switzerland) · 2024Review
- Genome-wide analysis of soybean hypoxia inducible gene domain containing genes: a functional investigation of GmHIGD3.Frontiers in plant science · 2024Article
- Early detection of abiotic stress in plants through SNARE proteins using hybrid feature fusion model.PeerJ. Computer science · 2024Article
- DEKR-SPrior: An Efficient Bottom-Up Keypoint Detection Model for Accurate Pod Phenotyping in Soybean.Plant phenomics (Washington, D.C.) · 2024Article
- Study on the Optimal Leaf Area-to-Fruit Ratio of Pear Trees on the Basis of Bearing Branch Girdling and Machine Learning.Plant phenomics (Washington, D.C.) · 2024Article
- Microbiome structure variation and soybean's defense responses during flooding stress and elevated COFrontiers in plant science · 2023Article
- High-throughput root phenotyping of crop cultivars tolerant to low N in waterlogged soils.Frontiers in plant science · 2023Article
- Phenotyping for waterlogging tolerance in crops: current trends and future prospects.Journal of experimental botany · 2022Review
- Capturing crop adaptation to abiotic stress using image-based technologies.Open biology · 2022Review
- Multi-Omics Techniques for Soybean Molecular Breeding.International journal of molecular sciences · 2022Review
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- The field phenotyping platform's next darling: Dicotyledons.Frontiers in plant science · 2022Review
- A review of remote sensing for potato traits characterization in precision agriculture.Frontiers in plant science · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. The flooding injury score (FIS) of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms. Aerial images were taken on the same day using a five-band multispectral and an infrared (IR) thermal camera at 20, 50, and 80 m above ground. Five image features, i.e., canopy temperature, normalized difference vegetation index, canopy area, width, and length, were extracted from the images at three flight heights. A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features. Results show that the image features were significantly different at three flight heights. The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS. The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.
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Registered trials
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