Apple Leaf Disease Identification Based on Optimized Deep Neural Network

Abstract

Apples are the most productive fruits in the world with a lot of medicinal and nutritional value. Significant economic losses occur frequently due to various diseases that occur on a huge scale of apple production. Consequently, the effective and timely discovery of apple leaf infection becomes compulsory. The proposed work uses optimal deep neural network for effectively identifying the diseases of apple trees. This work utilizes a convolution neural network to capture the features of Apple leaves. Extracted features are optimized with the help of the optimization algorithm. The optimized features are utilized in the leaf disease identification process. Here the traditional DNN algorithm is modified by means of weight optimization using adaptive monarch butterfly optimization (AMBO) algorithm. The experimental results show that the proposed disease identification methodology based on the optimized deep neural network accomplishes an overall accuracy of 98.42%.