Anthracnose Disease Detection in Papaya Fruit Using Machine Learning Techniques

The project focuses on predicting whether the papaya fruit is diseased or healthy. The main concern is anthracnose found in papaya fruit. The project was designed to help agriculture. We will use machine learning techniques to predict whether the fruit is sick or healthy. This project was designed to develop and evaluate learning models for predicting diseased and healthy fruits. The proposed method uses various classification algorithms, including decision trees, K-nearest neighbors (KNN), Naive Bayes, logistic regression, random forest, and common methods such as hard choices and soft choices. This study involves the collection of comprehensive information including different images of healthy and diseased fruits. Additionally, the project explores the benefits of integration by combining the results of each algorithm through hard voting and soft voting techniques. The goal of this combination is to increase distribution efficiency overall and power. As a result of the research, a helpful model that can determine if fruit is healthy or unhealthy will be produced. This project will be beneficial in the field of Agriculture and helpful for the farmers.