This study used close-range multispectral imagery over cucumber plants inside a commercial greenhouse to detect powdery mildew due to Podosphaera xanthii. It was collected using a MicaSense^® RedEdge camera at 1.5 m over the top of the plant. Image registration was performed using Speeded-Up Robust Features (SURF) with an affine geometric transformation. The image background was removed using a binary mask created with the aligned NIR band of each image, and the illumination was corrected using Cheng et al.’s algorithm. Different features were computed, including RGB, image reflectance values, and several vegetation indices. For each feature, a fine Gaussian Support Vector Machines algorithm was trained and validated to classify healthy and infected pixels. The data set to train and validate the SVM was composed of 1000 healthy and 1000 infected pixels, split 70–30% into training and validation datasets, respectively. The overall validation accuracy was 89, 73, 82, 51, and 48%, respectively, for blue, green, red, red-edge, and NIR band image. With the RGB images, we obtained an overall validation accuracy of 89%, while the best vegetation index image was the PMVI-2 image which produced an overall accuracy of 81%. Using the five bands together, overall accuracy dropped from 99% in the training to 57% in the validation dataset. While the results of this work are promising, further research should be considered to increase the number of images to achieve better training and validation datasets.

中文翻译：

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使用近距离多光谱图像检测受感染的黄瓜植株

本研究使用商业温室内黄瓜植物的近距离多光谱图像来检测由Podosphaera xanthii引起的白粉病。它是使用 MicaSense ^®收集的RedEdge 摄像头位于工厂顶部 1.5 m 处。使用具有仿射几何变换的加速鲁棒特征 (SURF) 进行图像配准。使用由每个图像的对齐 NIR 波段创建的二元掩模去除图像背景，并使用 Cheng 等人的算法校正照明。计算了不同的特征，包括 RGB、图像反射率值和几个植被指数。对于每个特征，都训练并验证了精细的高斯支持向量机算法，以对健康像素和受感染像素进行分类。用于训练和验证 SVM 的数据集由 1000 个健康像素和 1000 个感染像素组成，分别将 70-30% 分成训练和验证数据集。对于蓝色、绿色、红色、红色边缘，总体验证准确度分别为 89、73、82、51 和 48%，和 NIR 波段图像。对于 RGB 图像，我们获得了 89% 的整体验证精度，而最佳植被指数图像是 PMVI-2 图像，其整体精度为 81%。将五个波段一起使用，整体准确率从训练中的 99% 下降到验证数据集中的 57%。虽然这项工作的结果很有希望，但应考虑进一步研究以增加图像数量以实现更好的训练和验证数据集。