用卫星影像和机器学习，精准识别甘蔗隐性病害。
Machine Learning for Asymptomatic Ratoon Stunting Disease Detection With Freely Available Satellite Based Multispectral Imaging
- 基于卫星多光谱数据提取植被指数，结合多种机器学习模型
- SVM-RBF分类准确率最高达96.55%，不同品种差异显著
- 为大规模低成本病害监测提供新方案，适合农业遥感应用
甘蔗疾病检测，特别是隐性感染病害如根茎生长抑制病（RSD）的识别，对有效作物管理至关重要。本研究利用多种机器学习方法，基于免费获取的卫星多光谱数据衍生的植被指数，检测不同甘蔗品种中的RSD存在情况。结果表明，采用径向基函数核的支持向量机（SVM-RBF）表现最优，分类准确率在85.64%至96.55%之间，具体取决于品种；梯度提升与随机森林也表现出色，准确率范围为83.33%至96.55%；而逻辑回归和二次判别分析在不同品种间表现波动较大。甘蔗品种类型及植被指数的引入对RSD检测至关重要，与现有文献一致。本研究突显了基于卫星遥感技术在大规模甘蔗病害检测中的潜力，是传统人工实验室检测的低成本高效替代方案。
原文摘要 · Abstract (English)
Disease detection in sugarcane, particularly the identification of asymptomatic infectious diseases such as Ratoon Stunting Disease (RSD), is critical for effective crop management. This study employed various machine learning techniques to detect the presence of RSD in different sugarcane varieties, using vegetation indices derived from freely available satellite-based spectral data. Our results show that the Support Vector Machine with a Radial Basis Function Kernel (SVM-RBF) was the most effective algorithm, achieving classification accuracy between 85.64% and 96.55%, depending on the variety. Gradient Boosting and Random Forest also demonstrated high performance achieving accuracy between 83.33% to 96.55%, while Logistic Regression and Quadratic Discriminant Analysis showed variable results across different varieties. The inclusion of sugarcane variety and vegetation indices was important in the detection of RSD. This agreed with what was identified in the current literature. Our study highlights the potential of satellite-based remote sensing as a cost-effective and efficient method for large-scale sugarcane disease detection alternative to traditional manual laboratory testing methods.
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