Classification of Apple Leaf Diseases using Distinct Machine Learning Algorithms

Abstract

Early diagnosis and identification of plant leaf disease are important for long-term agriculture and maximum yield production. Deep Learning has developed as a powerful computing paradigm in the field of artificial intelligence, with the ability to handle a wide range of computer vision challenges. One of the deep learning architectures that suggest implicit results for image identification and object detection applications is the deep convolutional neural network (CNN). In this work, deep CNN models are used to identify and classify plant leaf diseases. We have used VGG16, VGG19, Inception V3 and analyzed their performance. Finally, machine learning techniques were used to determine if the new plant image was infected or not. The result verifies that using VGG16 model and LR classifier, an accuracy of 98.5% is obtained.