Classification of Custard Apple Leaves Using Deep Convolutional Networks

Abstract

Deep learning comes under the artificial intelligence does the work like the human brain in all the activities mainly in data processing and usage of those processed data in object detection, speech recognition, language translation and decision making. Convolutional neural network comes under the deep neural networks of deep learning, that is mainly applied to analyze the various images. The neural networks which are used to analyze the visual images are called as SIANN-shift invariant artificial neural networks that scan the convolutional neural networks layers and translation invariance characteristics on the basis of shared weight architecture. Forenhancedprotection of the plants from diseases, the widespread of the diseases can be controlled by early prediction of diseases in leaves. It also increased the production of the healthy plants. Since the custard apple has enormous medicinal uses, early identification of diseases in custard leaf will lead to the spread control of infection and development of healthy plants. The existing research techniques did not focused on the custard apple leaf diseases. The proposed work is an accurate predicting approach for classifying the custard apple leaves based on deep convolutional neural networks. The image dataset for custard apple leaves was created and modified general CNN architecture was used to classify the leaves. The accuracy achieved with the Adam optimizer is 85% for predicting the healthy and diseased leaves of custard apple.