Unmanned aerial vehicle-based assessment of rice leaf chlorophyll content dynamics across genotypes

Crop breeding programs have long faced the challenge of accurately collecting phenotypic information. The leaf chlorophyll content is an important growth indicator in rice breeding and is generally measured using a portable chlorophyll meter. In this study, a high-resolution RGB camera and a multispectral camera were mounted on unmanned aerial vehicles (UAVs) to obtain images of 216 hybrid rice varieties. Four different machine learning algorithms and were used to estimate the leaf chlorophyll content in each plot using 16 vegetation indices (VIs) calculated from the UAV-based images. The obtained results demonstrated that the chlorophyll content estimation performance of boosted regression trees (BRT) was better than random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR) models, with R2 = 0.712 and root mean square error (RMSE) = 1.524. The optimal model was utilized to analyze the rice chlorophyll content in the time series images through the inversion process, revealing a dynamic trend where the level of chlorophyll reached its highest point 82 days after transplantation (DAT). All rice varieties were grouped into four categories (Clusters A, B, C, and D) using nine dynamic indicators extracted from the chlorophyll content trend curve and the K-means clustering algorithm. According to Tukey's HSD tests (p < 0.05), the dynamic indicators showed significant differences between the four categories, especially for the minimum chlorophyll content (Cmin) and the difference between the maximum and final value (Cmax - Cf). The relationship between chlorophyll dynamic indicators and grain yield was analyzed and it was found that despite having the highest chlorophyll content and accumulating the most chlorophyll, Cluster A exhibited significantly lower grain yield. This was evident from the largest maximum chlorophyll content (Cmax) and the difference between maximum and initial value (Cmax - C0) values. The result implied that high chlorophyll content during a growing period or at a specific stage does not necessarily result in high yield. The findings in this study can provide new ideas and a basis for hybrid rice breeding.