Sistem Kecerdasan Buatan Untuk Deteksi Kondisi Daun Berbasis Metode Klasifikasi
Abstract
Plant diseases pose a significant threat to agricultural productivity. This study aims to develop and evaluate an artificial intelligence system capable of automatically detecting leaf health conditions and comparing the performance of two different deep learning architectures. Leaf image data obtained from the Kaggle dataset were processed and classified using Convolutional Neural Network (CNN) and MobileNetV2, while the YOLOv8 algorithm was applied to detect leaf objects within the images. The main evaluation metric used was classification accuracy to assess the models ability to identify whether a leaf is healthy or diseased. The results demonstrate the efficiency and comparative performance of both classification methods. The best-performing model was then implemented into a Python-based web application, enabling users to upload leaf images and obtain real-time health detection results. This implementation provides a practical contribution to the development of precision agriculture systems.
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