A robust rice yield estimation framework developed by grading modeling and normalized weight decision-making strategy using UAV imaging technology

文献类型： 外文期刊

第一作者： Yang, Rui

作者： Yang, Rui;Zhou, Jun;Lu, Xiangyu;Chen, Mengyuan;He, Yong;Liu, Fei;Zhou, Jun;Shen, Jianxun;Chen, Huizhe;Liu, Fei

作者机构：

关键词： Yield estimation; Grading; Weight decision-making; UAV image; Deep learning

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2023 年 215 卷

页码：

收录情况： SCI

摘要： Rice yield estimation is of great significance for ensuring food security and breeding new varieties with high yield and good stress resistance. The popular yield estimation method is to combine unmanned aerial vehicle (UAV) images to extract vegetation index (VI) for multi-variable regression, whose application is always limited by expensive equipment and complex data processing. In this study, based on the deep learning network, ConvNeXt, a robust framework developed by grading modeling and normalized weight decision-making strategy was innovatively proposed to estimate the rice yield, only using RGB images collected by UAVs. The main results are: (1) the yield estimation performance of yield grading model was better than that of regression model, and R2, mean absolute error (MAE), and mean absolute percentage error (MAPE) on the test dataset using the grading model were 0.97, 410.94 kg/ha, and 3.96 % respectively; (2) the confidence scores of the grading model were adopted as the weights, which could correct the estimated yield of the misclassified samples and further reduce the estimated error. The normalized weight decision-making after optimization had obvious advantages, the performance of 2-weight strategy was the best, whose R2, MAE, and MAPE reached 0.98, 386.08 kg/ha, and 3.79 %, respectively; (3) the MAPE results of generalization assessment showed that the generalization of the grading (12.98 %) and regression (10.88 %) models was inferior to the proposed framework (8.71 %), which could reduce the MAPE on the generalization evaluation dataset to less than 10 %. Furthermore, the framework exhibited good adaptability when applied to the rice yield estimation of the new data in 2022, with an MAPE of 4.54 %. Considering both application potential and adaptability, this framework constructs a novel strategy and method for rice yield estimation using grading modeling and normalized weight decision-making strategy, which provides a reference for future real-time and precise yield estimation using UAV remote sensing.

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