Researchers have shown increasing interest in hyperspectral imaging for detecting potato late blight disease (Phytophthora infestans). Because it is difficult to get accurate spectral signatures of disease development in field conditions, especially at early disease stages, previous works focused on laboratory measurements under controlled conditions. However, the extrapolation of results from a laboratory to a field setting has proven difficult. The current work evaluates the use of laboratory hyperspectral data to train an in-field detection model for potato late blight. A hyperspectral training library was constructed from six detached leaf trays, containing 8585 spectra labelled into a healthy class and five progressive stages of disease development. After smoothing and normalisation, a logistic regression model was trained on 70.0% of this data, with 30.0% reserved for validation. Twelve hyperspectral images taken in field conditions were then classified, for two potato cultivars (susceptible and resistant to late blight), at high and low disease pressure. The classification accuracy of laboratory data was 94.1%, which was not sufficient to detect field symptoms, using infield collected dataset. When spectra pre-processing was changed by including first derivation and adopting a new normalisation strategy, a new model resulted in a lower classification accuracy of 80.8%, validated on labelled laboratory spectra, but was able to detect symptoms in field conditions. The correlation between visual disease scoring and the classification result of the field disease model yielded an R² value of 0.985. It could be concluded that it was possible to train a model on laboratory data for in-field disease detection.

研究人员对用于检测马铃薯晚疫病（Phytophthora infestans）的高光谱成像越来越感兴趣）。由于很难在田间条件下获得疾病发展的准确光谱特征，尤其是在疾病早期阶段，以前的工作主要集中在受控条件下的实验室测量上。然而，从实验室到现场环境的结果外推已被证明是困难的。当前的工作评估了使用实验室高光谱数据训练马铃薯晚疫病现场检测模型的情况。高光谱训练库由六个分离的叶盘构建而成，其中包含 8585 个标记为健康类别和疾病发展的五个渐进阶段的光谱。在平滑和归一化之后，对 70.0% 的数据训练逻辑回归模型，其中 30.0% 保留用于验证。然后对在野外条件下拍摄的 12 张高光谱图像进行分类，对于两个马铃薯品种（易感和抗晚疫病），处于高病压和低病压。实验室数据的分类准确率为94.1%，不足以检测现场症状，使用现场收集的数据集。当光谱预处理通过包括一阶推导和采用新的归一化策略而改变时，新模型导致分类准确率降低 80.8%，在标记的实验室光谱上得到验证，但能够在现场条件下检测症状。视觉疾病评分与田间疾病模型分类结果之间的相关性产生了一个 R 使用内场收集的数据集。当光谱预处理通过包括一阶推导和采用新的归一化策略而改变时，新模型导致分类准确率降低 80.8%，在标记的实验室光谱上得到验证，但能够在现场条件下检测症状。视觉疾病评分与田间疾病模型分类结果之间的相关性产生了一个 R 使用内场收集的数据集。当光谱预处理通过包括一阶推导和采用新的归一化策略而改变时，新模型导致分类准确率降低 80.8%，在标记的实验室光谱上得到验证，但能够在现场条件下检测症状。视觉疾病评分与田间疾病模型分类结果之间的相关性产生了一个 R²值为 0.985。可以得出结论，可以训练实验室数据模型用于现场疾病检测。