ArticleScientific reports2019
Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Polyploidy and Mutagenic Germplasm Innovation in Minor Legumes: Paradigm Shift and Challenges from Model Crops to Mung Bean.Plants (Basel, Switzerland) · 2026Review
- UAV-based estimation of sunflower leaf flavonol content across multiple flight heights using RGB-multispectral canopy features and machine learning.Frontiers in plant science · 2026Article
- Physiological determinants of heterosis in hybrid rice (Frontiers in plant science · 2026Article
- Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US.Sensors (Basel, Switzerland) · 2025Article
- Confronting the data deluge: How artificial intelligence can be used in the study of plant stress.Computational and structural biotechnology journal · 2024Review
- Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review.Plants (Basel, Switzerland) · 2024Review
- Article
- Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation with Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughput Plant Phenotyping.Sensors (Basel, Switzerland) · 2024Article
- Color and Grey-Level Co-Occurrence Matrix Analysis for Predicting Sensory and Biochemical Traits in Sweet Potato and Potato.International journal of food science · 2024Article
- High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data.Plant methods · 2023Article
- Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.Frontiers in plant science · 2023Article
- Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models.Frontiers in plant science · 2023Article
- "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.Frontiers in plant science · 2023Article
- Multi-Omics Techniques for Soybean Molecular Breeding.International journal of molecular sciences · 2022Review
- Resources for image-based high-throughput phenotyping in crops and data sharing challenges.Plant physiology · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Global crop production is facing the challenge of a high projected demand, while the yields of major crops are not increasing at sufficient speeds. Crop breeding is an important way to boost crop productivity, however its improvement rate is partially hindered by the long crop generation cycles. If end-season crop traits such as yield can be predicted through early-season phenotypic measurements, crop selection can potentially be made before a full crop generation cycle finishes. This study explored the possibility of predicting soybean end-season traits through the color and texture features of early-season canopy images. Six thousand three hundred and eighty-three images were captured at V4/V5 growth stage over 6039 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix-based texture features were derived from each image. Another two variables were also introduced to account for location and timing differences between the images. Five regression and five classification techniques were explored. Best results were obtained using all 457 predictor variables, with Cubist as the regression technique and Random Forests as the classification technique. Yield (RMSE = 9.82, R
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