Bean Recommender System Using Machine Learning

The Bean Disease Detection and Recommendation System is a machine learning–based solution designed to help farmers identify bean diseases directly from images of affected leaves and receive appropriate recommendations.

The process begins when a farmer notices symptoms on a bean plant and uses a smartphone to take a clear picture of the affected leaf. The captured leaf image is submitted to the system for analysis.

The system uses computer vision to analyze the uploaded image and identify important visual patterns and symptoms on the bean leaf. The processed image is then passed to a trained Machine Learning model, which analyzes the extracted features and classifies the condition of the leaf.

After the analysis, the system provides the farmer with the detected disease name together with the model's prediction confidence. For example, the system may identify a leaf as having Angular Leaf Spot and display the confidence level of the prediction.

Based on the detected disease, the system then provides practical recommendations to help the farmer manage the identified problem. These recommendations may include appropriate disease-management practices, use of resistant bean varieties, recommended treatment options, proper field spacing and sanitation, and other preventive practices.

The complete system workflow is:

Take Photo of Bean Leaf → AI Leaf Analysis → Machine Learning Processing → Disease Detection → Treatment Recommendation

The system is intended to make early bean disease identification easier and more accessible to farmers by allowing them to use a smartphone from their farm. It demonstrates how machine learning and computer vision can transform a simple image of a bean leaf into useful disease information and practical recommendations that support better farming decisions.