Field Rice Growth Monitoring and Fertilization Management Based on UAV Spectral and Deep Image Feature Fusion

Abstract

Rice, as a globally vital staple crop, requires efficient field monitoring to ensure optimal growth conditions. This study proposed a novel framework for classifying nutrient deficiencies and formulating fertilization strategies in field-grown rice by fusing UAV-derived vegetation indices (VIs) with deep image features extracted via deep neural networks. The framework integrated visible light VIs, spectral VIs, and image features to provide a comprehensive reflection of crop nutritional conditions, aligning closely with practical production needs. The deep image features achieved nutrition classification accuracies of 88.78% and 84.56% for rice spikelet protection fertilizer application stage (S1) and bud-promoting fertilizer application stage (S2), while the fusion of VIs and deep image features significantly enhanced the accuracy of nutrient classification, with the RF model achieving the highest accuracy (97.50% in S1 and 96.56% in S2). The proposed fertilization strategy effectively improved rice growth traits, demonstrating the potential of UAV-based remote sensing for precision agriculture, which would provide a scalable solution for optimizing rice cultivation and ensuring food security.