Remote estimation of rapeseed phenotypic traits under different crop conditions based on unmanned aerial vehicle multispectral images

文献类型： 外文期刊

第一作者： Duan, Bo

作者： Duan, Bo;Xiao, Xiaolu;Huang, Fangyuan;Zhi, Ximin;Ma, Ni;Xie, Xiongze

作者机构：

关键词： rapeseed phenotyping; growth estimation; optical remote sensing; crop conditions; machine learning

期刊名称：JOURNAL OF APPLIED REMOTE SENSING （ 影响因子：1.4； 五年影响因子：1.4 ）

ISSN：

年卷期： 2024 年 18 卷 1 期

页码：

收录情况： SCI

摘要： . Rapeseed is an essential oil crop and the third major source of edible oil in the world. Accurate estimation of rapeseed phenotypic traits at field scale is important for precision agriculture to improve agronomic management and ensure edible oil supply. Unmanned aerial vehicle (UAV) remote sensing technology has been applied to estimate crop phenotypic traits at field scale. Machine learning is one of the main methods to develop estimation models for phenotypic traits based on UAV data. However, the accuracy and adaptability of machine learning estimation models are constrained by the representativeness of the training data. Here, we explored the influence of growth stage and crop conditions on the estimation of rapeseed phenotypic traits by machine learning and provided an optimized strategy to construct training data for improving the estimation accuracy. Four machine learning methods were employed, including partial least squares regression, support vector regression (SVR), random forest (RF), and artificial neural network (ANN), with SVR showing the best performance in estimating rapeseed phenotypic traits. The models established for a certain cultivar, planting site, or planting density had low estimation accuracies for other cultivars, planting sites, and planting densities during the entire growth period. The results showed that cultivar and planting site had an unquantifiable influence on phenotypic traits. Integration of stratified sampling and developing estimation models for different growth stages respectively can improve the estimation accuracy for different cultivars and planting sites during the entire growth period. Planting density exhibited a quantifiable influence on phenotypic traits, and the construction of training data with samples of both low and high planting densities could improve the estimation accuracy for different planting densities. Overall, optimization of the training data by considering the influence of crop conditions on phenotypic traits can improve the estimation accuracy of rapeseed phenotypic traits based on machine learning.

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