Identification of Chili Leaf Diseases Using DenseNet169 Architecture
DOI:
https://doi.org/10.37859/coscitech.v6i3.10631
Abstract
Chili is a high-value agricultural commodity in Indonesia, but its production is often hindered by leaf diseases such as spots, curling, and yellowing. Early identification of these diseases is crucial to prevent significant yield losses. This study aims to develop an automated system for identifying chili leaf diseases using the DenseNet169 Deep Learning architecture, implemented via a web-based platform. The methodology includes data collection from Roboflow.com (3,610 images of chili leaves across four classes: spots, curling, yellowing, and healthy), data preprocessing, augmentation, model training, and evaluation. The results demonstrate that the DenseNet169 model achieves an accuracy of 98%, with consistent precision, recall, and *F1-score* values for each class. The model is integrated into a Flask-based web application, allowing users to upload images of chili leaves for disease prediction and treatment recommendations. This system is expected to assist farmers in early disease detection, thereby improving cultivation efficiency and reducing crop failure risks.
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