roya rad; mohammad musavi; fatemeh vardi
Abstract
Rapid diagnosis of plant diseases has always been an important challenge for the agricultural industry. One of the approaches that has been welcomed in this field is the use of image processing methods. The advantage of these methods is that they are automatic, fast, low cost, non-destructive and accurate. ...
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Rapid diagnosis of plant diseases has always been an important challenge for the agricultural industry. One of the approaches that has been welcomed in this field is the use of image processing methods. The advantage of these methods is that they are automatic, fast, low cost, non-destructive and accurate. In this article efforts were focuced on distinguishing automatically healthy plants from unhealthy ones and also diagnosing the types and severity of their disease, by processing the images of the leaves of plants and agricultural products. To do this, deep learning-based methods including several different architectures of convolutional neural networks were used along with a support vector machine classifier. The proposed method in this study can be generalized to different plants and products as well as several plants simultaneously. The designed networks were evaluated using two different subsets of Plant Village data sets. In the first subset, which was related to the diagnosis of apple tree disease in four different classes, the accuracy was 95%, and in the second subset, which was related to four different plants in ten classes, the accuracy was 96.8%. Evaluation results showed that combining the support vector machine classifier with deep learning networks improved plant disease detection accuracy.
