Data from: Hyperspectral Imaging Analysis for Early Detection of Tomato Bacterial Leaf Spot Disease

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）技术已展现出良好的应用潜力，但目前仍缺乏经过验证的、兼具高空间与光谱分辨率的HSI数据集，用以表征不同叶片病害进程阶段的植株响应特征。为填补该研究空白，本研究以番茄细菌性叶斑病（病原菌为穿孔黄单胞菌Xanthomonas perforans）作为模式研究系统，对番茄叶片开展连续7天的高光谱成像，并以植株体内的病原菌种群量作为验证基准，分析染病与健康叶片间的光谱差异。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4个病害进程阶段训练机器学习模型，结果显示，HSI技术可在症状前阶段检测番茄叶片的病害，并能够区分细菌性叶斑与非生物胁迫诱导的叶斑。