LDIS: Leaf Disease Identification System Using Image Processing and Machine Learning

Leaf diseases can have a considerable influence on crop production and food security. Therefore, it's crucial to detect and diagnose these diseases early to prevent their spread and minimize yield losses. Image processing (IP), Machine-Learning (ML) algorithms and Deep Learning (DL) have been demonstrated to be effective and precise tools for plant disease detection. This research proposes a system Leaf Disease Identification System (LDIS) for detection of plant leaf diseases using Convolutional Neural Networks (CNNs). Image processing technique Gaussian Mixture Model is used for segmentation of disease affected region in potato leaves. We have also proposed our shape-extraction algorithm for extraction of shape feature of a leaf. Moreover, the performance is tested and trained in standard publicly available dataset. The accuracy achieved is 96.58%. Ultimately, the integration of image processing and machine learning algorithms presents a promising solution for timely and accurate detection of plant leaf diseases, which can aid in the early detection, management and control of these diseases.