Plants Leaf Diseases Detection Using Deep Learning

Abstract

Agriculture improvement is a national economic issue that extremely depends on productivity. The explanation of disease detection in plants plays a significant role in the agriculture field. Accurate prediction of the plant disease can help treat the leaf as early as possible, which controls the economic loss. This paper aims to use the Image processing techniques with Convolutional Neural Network (CNN). It is one of the deep learning techniques to classify and detect plant leaf diseases. A publicly available Plant village dataset was used, which consists of 15 classes, including 12 diseases classes and 3 healthy classes. The data augmentation techniques have been used. In addition to dropout and weight regularization, which leads to good classification results by preventing the model from over fitting. The model was optimized with the Adam optimization technique. The obtained results in terms of performance were 98.08% in the testing stage and 99.24% in the training stage. Next, the baseline model was improved using early stopping, and the accuracy increased to 98.34% on the testing set and 99.64% on the training set. The substantial success rate makes it a valuable advisory method to detect and identify transparently.

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