ArticlePlant methods2020
Yield prediction by machine learning from UAS-based mulit-sensor data fusion in soybean.
Article in Plant methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- UAV remote sensing for yield prediction in staple crops: a review.Frontiers in plant science · 2026Pooled it
- Establishing a knowledge structure for yield prediction in cereal crops using unmanned aerial vehicles.Frontiers in plant science · 2024Pooled it
- Sequential Soybean Phenotypes for Grain Yield Prediction in Reciprocal Two-Season Validation.Plants (Basel, Switzerland) · 2026Article
- Enhancing crop yield prediction accuracy with a novel interpretable deep learning model: MHCNN-LSTM-MHA.Scientific reports · 2026Article
- Optimized substrate selection for enhanced orchid growth based on high-throughput lysimetric arrays.Journal of Zhejiang University. Science. B · 2025Article
- Integrating UAV-Derived Diameter Estimations and Machine Learning for Precision Cabbage Yield Mapping.Sensors (Basel, Switzerland) · 2025Article
- Soybean yield estimation and lodging discrimination based on lightweight UAV and point cloud deep learning.Plant phenomics (Washington, D.C.) · 2025Article
- Mapping 1-km soybean yield across China from 2001 to 2020 based on ensemble learning.Scientific data · 2025Article
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Comparative analysis of adaptive and general labeling methods for soybean leaf detection.Frontiers in plant science · 2025Article
- Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review.Plants (Basel, Switzerland) · 2024Review
- Mapping rapeseed (Frontiers in plant science · 2024Article
- A Novel Approach to Pod Count Estimation Using a Depth Camera in Support of Soybean Breeding Applications.Sensors (Basel, Switzerland) · 2023Article
- Multi-Omics Techniques for Soybean Molecular Breeding.International journal of molecular sciences · 2022Review
- Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review.Journal of advanced research · 2022Review
- Response of a Diverse European Soybean Collection to "Short Duration" and "Long Duration" Drought Stress.Frontiers in plant science · 2022Article
- Beyond the traditional NDVI index as a key factor to mainstream the use of UAV in precision viticulture.Scientific reports · 2021Article
- Creeping Bentgrass Yield Prediction With Machine Learning Models.Frontiers in plant science · 2021Article
- Network-based approaches for understanding gene regulation and function in plants.The Plant journal : for cell and molecular biology · 2020Review
- Canopy Roughness: A New Phenotypic Trait to Estimate Aboveground Biomass from Unmanned Aerial System.Plant phenomics (Washington, D.C.) · 2020Article
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3 authors.
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Abstract
backgroundNowadays, automated phenotyping of plants is essential for precise and cost-effective improvement in the efficiency of crop genetics. In recent years, machine learning (ML) techniques have shown great success in the classification and modelling of crop parameters. In this research, we consider the capability of ML to perform grain yield prediction in soybeans by combining data from different optical sensors via RF (Random Forest) and XGBoost (eXtreme Gradient Boosting). During the 2018 growing season, a panel of 382 soybean recombinant inbred lines were evaluated in a yield trial at the Agronomy Center for Research and Education (ACRE) in West Lafayette (Indiana, USA). Images were acquired by the Parrot Sequoia Multispectral Sensor and the S.O.D.A. compact digital camera on board a senseFly eBee UAS (Unnamed Aircraft System) solution at R4 and early R5 growth stages. Next, a standard photogrammetric pipeline was carried out by SfM (Structure from Motion). Multispectral imagery serves to analyse the spectral response of the soybean end-member in 2D. In addition, RGB images were used to reconstruct the study area in 3D, evaluating the physiological growth dynamics per plot via height variations and crop volume estimations. As ground truth, destructive grain yield measurements were taken at the end of the growing season.
resultsAlgorithms and feature extraction techniques were combined to develop a regression model to predict final yield from imagery, achieving an accuracy of over 90.72% by RF and 91.36% by XGBoost.
conclusionsResults provide practical information for the selection of phenotypes for breeding coming from UAS data as a decision support tool, affording constant operational improvement and proactive management for high spatial precision.
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