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.

近期，在超光谱成像（HSI）技术应用于早期疾病检测方面的研究进展已展现出极大的潜力，然而，目前尚缺乏代表植物在叶片疾病发展不同阶段的响应的高分辨率（空间和光谱）HSI数据。为填补这一空白，本研究以番茄细菌性叶斑病（Xanthomonas perforans）作为模型系统。通过将番茄叶片的超光谱图像与连续七天的体内病原体种群进行验证，分析了感染叶片与健康叶片之间的差异。利用叶片级别的完整光谱数据、叶片级别的植被指数（VI）数据以及像素级别的完整光谱数据，在四个疾病发展阶段对机器学习模型进行训练。研究结果表明，HSI能够在番茄叶片的潜伏期检测疾病，并区分细菌性叶斑与无机叶斑。