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One of the most major occupations and a key contributor to the GDP of our country is agriculture. Food crops have a vital role in the ecosystem and for human health, and plant diseases can result in large losses in food supply. Plant leaves can become infected with a variety of diseases, including blights, leaf spots, and other bacterial and fungal infections. It's critical to identify plant diseases in order to minimise yield losses. Disease identification and plant monitoring are essential to sustainable agriculture. Manually observing plant diseases is a challenging task. We therefore present a novel solution to these issues. We are going to talk about a method that uses photos of the plantsleaves to identify plant illnesses. These flaws have a severe detrimental impact on both the quantity and quality of crop yield. In this research, a review is carried out to examine the outcomes of studies based on the machine learning model for plant leaf disease identification. Many plants die as a result of improper diagnosis of these illnesses, ignorance of the symptoms, and lack of knowledge about treatment options. Image processing, which can extract important information and features from images, is the primary component employed in this research. To achieve the maximum level of precision, leaf colour, leaf damage, leaf area, and leaf texture are all classified qualities. CNN algorithm is used to examine various image parameters or features for the purpose of recognising various plant leaf diseases. This plant disease diagnostics can assist farmers in creating defence systems that lead to robust and fruitful food crops.

Copyright © 2025 KHIZER KHAN. This is an open access article distributed under the Creative Commons Attribution License.