Data from: <b>Hyperspectral Imaging Analysis for Early Detection of Tomato Bacterial Leaf Spot Disease</b>

Recent advancements in hyperspectral imaging (HSI) for early disease detection have shown promising results, yet there is a lack of validated high-resolution (spatial and spectral) HSI data representing the responses of plants at different stages of leaf disease progression. To address these gaps, we used bacterial leaf spot (Xanthomonas perforans) of tomato as a model system. Hyperspectral images of tomato leaves, validated against in planta pathogen populations for seven consecutive days, were analyzed to reveal differences between infected and healthy leaves. Machine learning models were trained using leaf-level full spectra data, leaf-level Vegetation index (VI) data, and pixel-level full spectra data at four disease progression stages. The results suggest that HSI can detect disease on tomato leaves at pre-symptomatic stages and differentiate bacterial disease spots from abiotic leaf spots.近年来，面向病害早期检测的高光谱成像（hyperspectral imaging, HSI）技术已取得颇具潜力的研究进展，但当前仍缺乏经过验证的、兼具空间与光谱分辨率的高分辨率HSI数据集，用以表征不同叶片病害进程阶段中植株的响应特征。为填补上述研究空白，本研究以番茄细菌性叶斑病（Xanthomonas perforans）为模式体系。研究人员采集番茄叶片的高光谱图像，并连续7天结合植株体内病原菌种群数量进行验证分析，以揭示染病叶片与健康叶片间的特征差异。本研究分别采用叶片级全光谱数据、叶片级植被指数（Vegetation index, VI）数据以及像素级全光谱数据，针对四个病害进程阶段训练机器学习模型。研究结果表明，高光谱成像可在症状前阶段检测番茄叶片的病害，并可有效区分细菌性叶斑与非生物性叶斑。