用NDVI和神经网络实现作物健康精准分类，准确率达97.8%。
Remote Sensing Based Crop Health Classification Using NDVI and Fully Connected Neural Networks
- 结合遥感NDVI数据与全连接神经网络进行分类
- 在多区域卫星图像上达到97.80%的准确率
- 适合农业监测、智慧农场及粮食安全研究者
精准的作物健康监测对提升农业效率和保障可持续粮食生产至关重要。传统方法依赖人工观察或简单NDVI测量，难以捕捉作物胁迫与病害的细微差异。本文提出一种更先进的方法：利用来自多个农业区的卫星影像训练全连接神经网络（FCNN），结合NDVI数据对作物健康状态进行分类。该模型可有效区分健康作物、锈病感染植株及其他胁迫状况。实验结果显示，该方法分类准确率达97.80%，在精确率、召回率和F1分数上均显著优于传统模型。基于深度学习建立的NDVI值与作物健康关系映射，为大范围、实时农业监测提供了高效方案，减少人工成本，助力全球粮食安全。
原文摘要 · Abstract (English)
Accurate crop health monitoring is not only essential for improving agricultural efficiency but also for ensuring sustainable food production in the face of environmental challenges. Traditional approaches often rely on visual inspection or simple NDVI measurements, which, though useful, fall short in detecting nuanced variations in crop stress and disease conditions. In this research, we propose a more sophisticated method that leverages NDVI data combined with a Fully Connected Neural Network (FCNN) to classify crop health with greater precision. The FCNN, trained using satellite imagery from various agricultural regions, is capable of identifying subtle distinctions between healthy crops, rust-affected plants, and other stressed conditions. Our approach not only achieved a remarkable classification accuracy of 97.80% but it also significantly outperformed conventional models in terms of precision, recall, and F1-scores. The ability to map the relationship between NDVI values and crop health using deep learning presents new opportunities for real-time, large-scale monitoring of agricultural fields, reducing manual efforts, and offering a scalable solution to address global food security.
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