This study explored rapid detection techniques for citrus Huanglongbing (HLB), a disease that severely impacts global citrus production. The method based on hyperspectral technology combined with machine learning algorithms provides new ideas for rapid HLB identification. Algorithm selection is crucial for processing efficiency and hyperspectral data interpretation. Hyperspectral data from healthy, mild HLB-infected, and macular (not related to HLB) citrus leaves were captured using a hyperspectrometer, with qPCR validation. Three preprocessing methods were selected to preprocess the spectral data. Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) were used to extract feature bands from the hyperspectral data, and the range of the number of filtered feature bands as a percentage of the full band was 22.87%–28.31% and 3.27%–4.17%, respectively. Five distinct algorithms were then employed to construct classification models. Upon evaluation, the SPA-STD-SVM algorithm combination proved most effective, boasting a 97.46% accuracy and a 98.55% recall rate. The results demonstrate that suitable machine learning algorithms can effectively classify the hyperspectral data of citrus leaves in three different states: healthy, mild HLB-infected, and macular. This provides an effective approach for using hyperspectral data to differentiate citrus Huanglongbing.

中文翻译：

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柑橘黄龙病检测：一种将特征带选择与机器学习算法相结合的高光谱数据驱动模型


本研究探讨了柑橘黄龙病 （HLB） 的快速检测技术，HLB 是一种严重影响全球柑橘生产的疾病。该方法基于高光谱技术结合机器学习算法，为快速识别 HLB 提供了新思路。算法选择对于处理效率和高光谱数据解释至关重要。使用超光谱仪捕获来自健康、轻度 HLB 感染和黄斑（与 HLB 无关）柑橘叶的高光谱数据，并进行 qPCR 验证。选择了 3 种预处理方法对光谱数据进行预处理。采用竞争性自适应重加权采样 （CARS） 和连续投影算法 （SPA） 从高光谱数据中提取特征带，滤波后特征带数占全波段的百分比范围分别为 22.87%–28.31% 和 3.27%–4.17%。然后采用五种不同的算法来构建分类模型。经评估，SPA-STD-SVM 算法组合证明最有效，准确率为 97.46%，召回率为 98.55%。结果表明，合适的机器学习算法可以有效地将柑橘叶片的高光谱数据分为三种不同状态：健康、轻度 HLB 感染和黄斑。这为利用高光谱数据区分柑橘黄龙病提供了一种有效的方法。