Version 1
: Received: 7 May 2024 / Approved: 7 May 2024 / Online: 7 May 2024 (16:47:22 CEST)
Version 2
: Received: 8 May 2024 / Approved: 23 May 2024 / Online: 23 May 2024 (12:31:37 CEST)
How to cite:
Yang, S.; Li, L.; Fei, S.; Yang, M.; Tao, Z.; Meng, Y.; Xiao, Y. Wheat Yield Estimation Using Machine Learning Method Based on UAV Remote Sensing Data. Preprints2024, 2024050402. https://doi.org/10.20944/preprints202405.0402.v1
Yang, S.; Li, L.; Fei, S.; Yang, M.; Tao, Z.; Meng, Y.; Xiao, Y. Wheat Yield Estimation Using Machine Learning Method Based on UAV Remote Sensing Data. Preprints 2024, 2024050402. https://doi.org/10.20944/preprints202405.0402.v1
Yang, S.; Li, L.; Fei, S.; Yang, M.; Tao, Z.; Meng, Y.; Xiao, Y. Wheat Yield Estimation Using Machine Learning Method Based on UAV Remote Sensing Data. Preprints2024, 2024050402. https://doi.org/10.20944/preprints202405.0402.v1
APA Style
Yang, S., Li, L., Fei, S., Yang, M., Tao, Z., Meng, Y., & Xiao, Y. (2024). Wheat Yield Estimation Using Machine Learning Method Based on UAV Remote Sensing Data. Preprints. https://doi.org/10.20944/preprints202405.0402.v1
Chicago/Turabian Style
Yang, S., Yaxiong Meng and Yonggui Xiao. 2024 "Wheat Yield Estimation Using Machine Learning Method Based on UAV Remote Sensing Data" Preprints. https://doi.org/10.20944/preprints202405.0402.v1
Abstract
Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. 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, and 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 prediction accuracy of wheat yield. 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 demonstrated that the third-layer ensemble method not only augments the predictive ability of the model but also fine-tuned the accuracy of wheat yield prediction through the employment of simple average ensemble learning. Consequently, it offers a novel perspective for crop yield prediction and breeding selection.
Keywords
Machine learning; Yield prediction; Data fusion; Wheat; Phenotyping
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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