Field Rice Growth Monitoring and Fertilization Management Based on UAV Spectral and Deep Image Feature Fusion
Bingnan Chen, Qiang Su, Yansong Li, Rui Chen, Wanneng Yang, Chenglong Huang
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.