Paper Title
DEEP LEARNING-BASED DETECTION AND IDENTIFICATION OF PLANT LEAF DISEASES

Abstract
For better crop yield and reduced agricultural loss, plant leaf diseases must be diagnosed early and accurately. In this article, we introduce Convolutional Neural Networks (CNNs), a deep learning approach, for disease detection and classification automatically based on plant leaf images. Preprocessing, segmentation, feature extraction, classification, and image acquisition are incorporated into the process. 17,430 diseased and healthy labelled images of tomato, potato, and bell pepper leaves from 14 classes formed the PlantVillage dataset on which the system was trained. The proposed CNN model is superior to traditional machine learning models such as SVM, KNN, logistic regression, and decision trees and is 85.31% accurate. The robustness of the model was also tested using a smaller data set of 500 images, which was 94% accurate with segmentation using genetic algorithms. The results indicate that the method is effective and scalable and can be used offline or in mobile applications to help farmers in remote or resource-poor areas. Keywords - Deep Learning, CNN, Image Segmentation, Image Processing, Plant Village Dataset