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 R2 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.
Keywords: UAV; agronomic trait; machine learning; remote sensing; yield prediction.
Copyright © 2023 Zhou, Yang, Lou, Sheng, Li and Hu.