Hyperspectral imaging allows for rapid, non-destructive and objective assessments of crop health. Narrowband-hyperspectral data was used to select wavelength regions that can be exploited to identify wheat infected with soil-borne mosaic virus. First, leaf samples were scanned in the lab to investigate spectral differences between healthy and diseased leaves, including non-symptomatic and symptomatic areas within a diseased leaf. The potential of 84 commonly used vegetation indices to find infection was explored. A machine-learning approach was used to create a classification model to automatically separate pixels into symptomatic, non-symptomatic and healthy classes. The success rate of the model was 69.7% using the full spectrum. It was very encouraging that by using a subset of only four broad bands, sampled to simulate a data set from a much simpler and less costly multispectral camera, accuracy increased to 71.3%. Next, the classification models were validated on field data. Infection in the field was successfully identified using classifiers trained on the entire spectrum of the hyperspectral data acquired in a lab setting, with the best accuracy being 64.9%. Using a subset of wavelengths, simulating multispectral data, the accuracy dropped by only 3 percentage points to 61.9%. This research shows the potential of using lab scans to train classifiers to be successfully applied in the field, even when simultaneously reducing the hyperspectral data to multispectral data.

高光谱成像可以对作物健康状况进行快速、无损和客观的评估。窄带高光谱数据用于选择可用于识别感染土传花叶病毒的小麦的波长区域。首先，在实验室扫描叶片样本以研究健康叶片和患病叶片之间的光谱差异，包括患病叶片内的无症状和有症状区域。探索了 84 种常用植被指数发现感染的潜力。使用机器学习方法创建分类模型，自动将像素分为有症状、无症状和健康类别。该模型使用全光谱的成功率为 69.7%。非常令人鼓舞的是，通过仅使用四个宽带的子集，从更简单、成本更低的多光谱相机中采样以模拟数据集，准确率提高到 71.3%。接下来，根据现场数据验证分类模型。使用在实验室环境中获取的高光谱数据的整个光谱上训练的分类器成功识别了现场感染，最佳准确度为 64.9%。使用波长的一个子集，模拟多光谱数据，准确度仅下降了 3 个百分点至 61.9%。这项研究显示了使用实验室扫描来训练分类器以成功应用于该领域的潜力，即使同时将高光谱数据减少为多光谱数据也是如此。使用在实验室环境中获取的高光谱数据的整个光谱上训练的分类器成功识别了现场感染，最佳准确度为 64.9%。使用波长的一个子集，模拟多光谱数据，准确度仅下降了 3 个百分点至 61.9%。这项研究显示了使用实验室扫描来训练分类器以成功应用于该领域的潜力，即使同时将高光谱数据减少为多光谱数据也是如此。使用在实验室环境中获取的高光谱数据的整个光谱上训练的分类器成功识别了现场感染，最佳准确度为 64.9%。使用波长的一个子集，模拟多光谱数据，准确度仅下降了 3 个百分点至 61.9%。这项研究显示了使用实验室扫描来训练分类器以成功应用于该领域的潜力，即使同时将高光谱数据减少为多光谱数据也是如此。