Potato diseases detection and classification using deep learning methods

Using machine vision and image processing methods has an important role in the identification of defects of agricultural products, especially potatoes. The applications of image processing and artificial intelligence in agriculture in identifying and classifying pests and diseases of plants and fruits have increased and research in this field is ongoing. In this paper, we use the convolution neural network (CNN) methods, also, we examined 5 classes of potato diseases with the names: Healthy, Black Scurf, Common Scab, Black Leg, Pink Rot. We used a database of 5000 potato images. We compared the results of potato defect classification our methods with other methods such as Alexnet, Googlenet, VGG, R-CNN, Transfer Learning. The results show that the accuracy of the deep learning proposed method is higher than other existing works. We get 100% and 99% accuracy in some of the classes, respectively.

Automated summary

Using machine vision and image processing methods plays an important role in identifying defects in agricultural products, particularly potatoes. Research has shown that applying image processing and artificial intelligence in agriculture can improve the accuracy of identifying and classifying pests and diseases. In this study, a convolution neural network (CNN) method was used to analyze five classes of potato diseases, and the results were compared with other methods. The findings demonstrate that the proposed deep learning method achieved higher accuracy compared to existing works.