Smart Mobile Application for Pomegranate Disease Detection

Computer Engineering

Pomegranates are a highly valuable fruit that grow in profusion in various Asian nations. However a variety of reasons cause the plants to become afflicted with several diseases that decimate the entire crop, resulting in catastrophically low product production. This research presents an image processing, supervised and unsupervised technique to healthiness detection and categorization of pomegranate leaf to address the challenges in phytopathology. The environment and diseases that are transferred by insects have an impact on the leaves. These conditions include rot, spot, and the blight bacterium, for instance. The suggested system uses various images for testing and training purposes. To isolate the leaf damage, a colour image must first be preprocessed using k-means clustering. The pre-processing procedure includes locating the Area of Interest (ROI), scaling, colour conversion, and filtering. The next step is to extract features and classify the data using supervised learning models like ResNet and MobileNet. The best-performing model is found using classification criteria.

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