Abstract
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.
Highlights
Greenhouse production in Canada in 2019 comprised about 838 specialized commercial greenhouses covering more than 17.6 million m2, generating more than $1.1 billion in revenue, and employing more than 12,429 persons [1]
We evaluated the feasibility of using multispectral images acquired at close-distance over greenhouse cucumber plants to detect powdery mildew using a
The use of close-range distance imagery for plant disease detection, had been previously reported: Thomas et al [69] reported a distance of 80 cm from the top of the canopy of six barley cultivars in a study of their susceptibility to powdery mildew caused by Blumeria graminis f. sp
Summary
Greenhouse production in Canada in 2019 comprised about 838 specialized commercial greenhouses covering more than 17.6 million m2 (mainly in Ontario), generating more than $1.1 billion in revenue, and employing more than 12,429 persons [1]. Fungal diseases can affect greenhouse crops and be a significant limiting production factor [2]. Powdery mildew is one cucumber plant disease that can cause yield losses of 30–50% [3]. Approaches based on temperature and relative humidity have been proposed as early warning methods [5,6,7]. Other methods include periodic visual inspection of the plants by technicians. This approach is time-consuming, costly, and does not collect spatial information. It can be unreliable as leaves that look healthy can be infected. Early disease detection is needed for Remote Sens.
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