ArticleFrontiers in plant science2020
Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed.
- Sequential Soybean Phenotypes for Grain Yield Prediction in Reciprocal Two-Season Validation.Plants (Basel, Switzerland) · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Integrating hyperspectral reflectance and machine learning for rapid diagnosis of nutrient deficiencies in greenhouse chrysanthemum leaves.BMC plant biology · 2026Article
- FloraSyntropy-net: scalable deep learning with novel FloraSyntropy archive for large-scale plant disease diagnosis.Plant methods · 2026Article
- Full-Spectrum Hyperspectral Modeling of Leaf Dry Matter Content Using a Stacked Ensemble Framework.Sensors (Basel, Switzerland) · 2026Article
- Predicting Multiple Traits of Rice and Cotton Across Varieties and Regions Using Multi-Source Data and a Meta-Hybrid Regression Ensemble.Sensors (Basel, Switzerland) · 2026Article
- Investigating the Association Between Citrus Huanglongbing and Chlorophyll Content Using Hyperspectral Detection.Sensors (Basel, Switzerland) · 2025Article
- Machine learning models for predicting morphological traits and optimizing genotype and planting date in roselle (Hibiscus Sabdariffa L.).Scientific reports · 2025Article
- Deep learning-based automatic diagnosis of rice leaf diseases using ensemble CNN models.Scientific reports · 2025Article
- Soybean yield estimation and lodging discrimination based on lightweight UAV and point cloud deep learning.Plant phenomics (Washington, D.C.) · 2025Article
- Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines.Sensors (Basel, Switzerland) · 2025Article
- Artificial intelligence in financial market prediction: advancements in machine learning for stock price forecasting.Frontiers in artificial intelligence · 2025Review
- Enhancing buckwheat maturity classification with generative adversarial networks for spectroscopy data augmentation.Frontiers in plant science · 2025Article
- Multi-sensor and multi-temporal high-throughput phenotyping for monitoring and early detection of water-limiting stress in soybean.Plant phenome journal · 2024Article
- Application of machine learning algorithms for predicting the life-long physiological effects of zinc oxide Micro/Nano particles on Carum copticum.BMC plant biology · 2024Article
- Choosing an appropriate somatic embryogenesis medium of carrot (Daucus carota L.) by data mining technology.BMC biotechnology · 2024Article
- Review
- Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling.Scientific reports · 2024Article
- A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction.Scientific reports · 2024Article
- Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles.Foods (Basel, Switzerland) · 2024Article
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5 authors.
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Abstract
Recent substantial advances in high-throughput field phenotyping have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits at early growth stages. Nevertheless, the implementation of large datasets generated by high-throughput phenotyping tools such as hyperspectral reflectance in cultivar development programs is still challenging due to the essential need for intensive knowledge in computational and statistical analyses. In this study, the robustness of three common machine learning (ML) algorithms, multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were evaluated for predicting soybean (
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