ArticleFrontiers in plant science2021
Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Establishing a knowledge structure for yield prediction in cereal crops using unmanned aerial vehicles.Frontiers in plant science · 2024Pooled it
- A Thermal Infrared Remote Sensing Model for Diagnosing Winter Wheat Water (Plants (Basel, Switzerland) · 2026Article
- Spatiotemporal dynamics and forecasting of public attention to entrepreneurship education: An entropy-based modeling approach.PloS one · 2026Article
- Integrating sensor fusion with machine learning for comprehensive assessment of phenotypic traits and drought response in poplar species.Plant biotechnology journal · 2025Article
- Soybean leaf estimation based on RGB images and machine learning methods.Plant methods · 2023Article
- Evaluation of Physicochemical Properties of Sustained-Release Membranes Based on Analytic Hierarchy.Membranes · 2023Article
- Research hotspots and frontiers in agricultural multispectral technology: Bibliometrics and scientometrics analysis of the Web of Science.Frontiers in plant science · 2022Article
- Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery.Frontiers in plant science · 2022Article
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8 authors.
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Abstract
Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern precision agriculture. Recent advances in an unmanned aerial vehicle (UAV) and development of sensors have opened a new avenue for data acquisition cost-effectively and rapidly. We evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments. For this, multispectral vegetation indices (VIs) and normalized relative canopy temperature (NRCT) were calculated and selected by the gray relational analysis (GRA) at each growth stage, i.e., jointing, booting, heading, flowering, grain filling, and maturity to reduce the data dimension. The elastic net regression (ENR) was developed by using selected features as input variables for yield prediction, whereas the entropy weight fusion (EWF) method was used to combine the predicted GY values from multiple growth stages. In our results, the fusion of dual-sensor data showed high yield prediction accuracy [coefficient of determination (
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