Implementasi CNN untuk Identifikasi Penyakit Daun Cabai
Implementation of CNN for Chili Leaf Disease Identification
DOI:
https://doi.org/10.37859/coscitech.v6i3.9381
Abstract
Disease detection in chili plants is a crucial step in preventing damage that can reduce productivity and cause economic losses for farmers. This study presents the design of an Android-based application called Chili Leaf Disease App that can automatically detect chili leaf diseases. The application uses a Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture to classify leaf diseases through images captured directly from the camera or uploaded from the gallery. The dataset used consists of 4,000 chili leaf images across four disease classes. Testing results show that the model achieves an accuracy of 97.5%. The system was developed using the Rapid Application Development (RAD) method, chosen for its shorter development cycle, flexibility, and ability to increase user involvement. This approach enables efficient, fast, and user-responsive application development. The application is expected to help farmers detect diseases early and take preventive action more quickly to maintain plant health.
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References
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