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Improving grain yield prediction through fusion of multi-temporal spectral features and agronomic trait parameters derived from UAV imagery

文献类型： 外文期刊

作者： Zhou, Hongkui 1 ; Yang, Jianhua 2 ; Lou, Weidong 1 ; Sheng, Li 1 ; Li, Dong 1 ; Hu, Hao 1 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou, Peoples R China

2.Tianjin Normal Univ, Acad Ecocivilizat Dev Jing Jin Ji Megalopolis, Tianjin, Peoples R China

关键词： yield prediction; agronomic trait; remote sensing; UAV; machine learning

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.6； 五年影响因子：6.8 ）

ISSN： 1664-462X

年卷期： 2023 年 14 卷

页码：

收录情况： SCI

摘要： Rapid and accurate prediction of crop yield is particularly important for ensuring national and regional food security and guiding the formulation of agricultural and rural development plans. Due to unmanned aerial vehicles' ultra-high spatial resolution, low cost, and flexibility, they are widely used in field-scale crop yield prediction. Most current studies used the spectral features of crops, especially vegetation or color indices, to predict crop yield. Agronomic trait parameters have gradually attracted the attention of researchers for use in the yield prediction in recent years. In this study, the advantages of multispectral and RGB images were comprehensively used and combined with crop spectral features and agronomic trait parameters (i.e., canopy height, coverage, and volume) to predict the crop yield, and the effects of agronomic trait parameters on yield prediction were investigated. The results showed that compared with the yield prediction using spectral features, the addition of agronomic trait parameters effectively improved the yield prediction accuracy. The best feature combination was the canopy height (CH), fractional vegetation cover (FVC), normalized difference red-edge index (NDVI_RE), and enhanced vegetation index (EVI). The yield prediction error was 8.34%, with an R-2 of 0.95. The prediction accuracies were notably greater in the stages of jointing, booting, heading, and early grain-filling compared to later stages of growth, with the heading stage displaying the highest accuracy in yield prediction. The prediction results based on the features of multiple growth stages were better than those based on a single stage. The yield prediction across different cultivars was weaker than that of the same cultivar. Nevertheless, the combination of agronomic trait parameters and spectral indices improved the prediction among cultivars to some extent.

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