Image-Based Papaya Fruit Health Classification Using Machine Learning

Papaya is a widespread fruit, but its production is heavily affected by a variety of diseases. Early detection of diseased papayas can help farmers take precautions to minimize harvest losses. This project presents a Papaya Fruit Disease Recognition System with machine learning technology (machine learning) to classify papayas as healthy or disease. Models were meticulously trained on diverse datasets to ensure robust performance of the disease. To evaluate the model output, K-fold Cross validation was used to test each segmentation method for each classification. Under all combinations, segmentation of fuzzy C-means combined with random forest achieved the highest accuracy. Secondly, Streamlit was provided to this optimized model, so users uploaded Papaya images and received the disease of instant disease classification.