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，用于病害早期检测的高光谱成像（hyperspectral imaging, HSI）技术已展现出颇具潜力的研究成果，但目前仍缺乏能够反映叶片在不同病害发展阶段响应的、经过验证的高空间与高光谱分辨率高光谱数据。为填补这一研究空白，本研究以番茄细菌性叶斑病（Xanthomonas perforans）作为模式体系。研究对连续7天采集、并与植株内病原菌种群进行对照验证的番茄叶片高光谱图像展开分析，以明确侵染叶片与健康叶片之间的差异。针对四个病害发展阶段，本研究分别利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训练机器学习模型。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，并能够区分细菌性叶斑与非生物胁迫引发的叶斑。

搜集汇总

背景与挑战

背景概述

该数据集包含用于番茄细菌性叶斑病早期检测的高光谱成像数据，通过机器学习模型分析叶片在不同疾病进展阶段的光谱差异，旨在实现症状前期的疾病检测和鉴别。数据集由USDA-NIFA资助，并附有相关研究论文的补充信息。