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Developing an Uncrewed Aerial Vehicle (UAV)-Based Prediction Model for the Rice Harvest Index Using Machine Learning

文献类型： 外文期刊

作者： Pan, Zhaoyang 1 ; Lu, Zhanhua 1 ; Zhang, Liting 1 ; Liu, Wei 1 ; Wang, Xiaofei 1 ; Wang, Shiguang 1 ; Chen, Hao 1 ; Wu, Haoxiang 1 ; Xu, Weicheng 1 ; Fu, Youqiang 1 ; He, Xiuying 1 ;

作者机构： 1.Guangdong Acad Agr Sci, Guangdong AcadAgr Sci Sci,Guangdong Rice Engn Lab, Key Lab Genet & Breeding High Qual Rice Southern C, Minist Agr & Rural Affairs,Guangdong Key Lab Rice, Guangzhou 510640, Guangdong, Peoples R China

关键词： rice; harvest index; UAV remote sensing; machine learning

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 9 期

页码：

收录情况： SCI

摘要： (1) Background: The harvest index is important for measuring the correlation between grain yield and aboveground biomass. However, the harvest index can only be measured after a mature harvest. If it can be obtained in advance during the growth period, it will promote research on high harvest indices and variety breeding; (2) Methods: In this study, we proposed a method to predict the harvest index during the rice growth period based on uncrewed aerial vehicle (UAV) remote sensing technology. UAV obtained visible light and multispectral images of different varieties, and the data such as digital surface elevation, visible light reflectance, and multispectral reflectance were extracted after processing for correlation analysis. Additionally, characteristic variables significantly correlated with the harvest index were screened out; (3) Results: The results showed that TCARI (correlation coefficient -0.82), GRVI (correlation coefficient -0.74), MTCI (correlation coefficient 0.83), and TO (correlation coefficient -0.72) had a strong correlation with the harvest index. Based on the above characteristics, this study used a variety of machine learning algorithms to construct a harvest index prediction model. The results showed that the Stacking model performed best in predicting the harvest index (R2 reached 0.88) and had a high prediction accuracy. (4) Conclusions: Therefore, the harvest index can be accurately predicted during rice growth through UAV remote sensing images and machine learning technology. This study provides a new technical means for screening high harvest index in rice breeding, provides an important reference for crop management and variety improvement in precision agriculture, and has high application potential.

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