Design and Implementation of a Machine Learning-Based Crop Disease Detection System
Computer Science — 2024, Undergraduate
This study designed and implemented a Crop Disease Detection System that uses machine learning to identify plant diseases from images of leaves. Early detection of crop diseases is critical to reducing agricultural losses, yet many farmers lack access to expert diagnosis. The system applies image classification to distinguish healthy from diseased crops and recommend appropriate action. Built with Python and Django, the system was trained and evaluated on a dataset of crop leaf images. The findings showed a high classification accuracy and a practical, accessible interface for farmers. The study recommends extending the system to more crops and integrating it into a mobile application for broader reach.
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