Detection of Jackfruit Leaf Disease Using Machine Learning and Deep Learning

Abstract: The detection of diseases in agricultural crops plays a critical role in maintaining healthy yields. Jackfruit (Artocarpus heterophyllus), a tropical fruit, is susceptible to various diseases that impact its leaves. Timely disease detection can significantly reduce crop loss and improve the quality of the harvest. This paper proposes a system for detecting jackfruit leaf diseases using machine learning (ML) and deep learning (DL) techniques. A dataset of healthy and diseased jackfruit leaf images is used to train both traditional ML algorithms (Random Forest) and DL models (Convolutional Neural Networks). The results indicate that deep learning models, particularly CNNs, outperform traditional ML models in terms of classification accuracy, precision, and recall. This system serves as an effective tool for early detection and management of jackfruit leaf diseases, offering an automated solution for farmers.