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

文献类型： 外文期刊

第一作者： Chen, Bingnan

作者： Chen, Bingnan;Su, Qihe;Li, Yansong;Chen, Rui;Huang, Chenglong;Chen, Bingnan;Yang, Wanneng;Huang, Chenglong;Chen, Bingnan;Yang, Wanneng;Huang, Chenglong;Yang, Wanneng;Huang, Chenglong

作者机构：

关键词： unmanned aerial vehicle; vegetation indices; deep learning and machine learning; feature fusion; fertilization management

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 4 期

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收录情况： SCI

摘要： 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.

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