Wheat Yield Prediction Using Machine Learning Method Based on UAV Remote Sensing Data

• Published in: Drones (Basel) Vol. 8; no. 7; p. 284
• Main Authors: Yang, Shurong, Li, Lei, Fei, Shuaipeng, Yang, Mengjiao, Tao, Zhiqiang, Meng, Yaxiong, Xiao, Yonggui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Accuracy; Agricultural production; Agriculture; Algorithms; Crop yield; Crop yields; Crops; Data integration; Datasets; Decision trees; Drone aircraft; Ensemble learning; Environmental aspects; Food supply; Forecasting; Growth; Infrared detectors; Machine learning; multi-sensor data fusion; Multisensor fusion; phenotyping; Physiology; Predictions; Remote sensing; Remote sensors; Sensors; Software; unmanned aerial vehicle; Unmanned aerial vehicles; Vegetation; Wheat; yield prediction; China; United States > US
• DOI: 10.3390/drones8070284
• ISSN: 2504-446X

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. Despite significant advancements in crop yield forecasting, existing methods often struggle with integrating diverse sensor data and achieving high prediction accuracy under varying environmental conditions. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms, namely random forest (RF), partial least squares (PLS), ridge regression (RR), k-nearest neighbor (KNN) and extreme gradient boosting decision tree (XGboost), were utilized for multi-sensor data fusion, together with three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average), for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB + MS + Texture + TIR combination demonstrating the highest fusion performance (R2 = 0.660, RMSE = 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the accuracy of wheat yield prediction. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE = 0.668 t ha−1). It significantly outperformed both the second-layer ensemble methods of stacking (R2 = 0.668, RMSE = 0.673 t ha−1) and feature-weighted (R2 = 0.667, RMSE = 0.674 t ha−1), thereby exhibiting superior predictive capabilities. This finding highlighted the third-layer ensemble method’s ability to enhance predictive capabilities and refined the accuracy of wheat yield prediction through simple average ensemble learning, offering a novel perspective for crop yield prediction and breeding selection.