Implementation of Convolutional Neural Network for Leaf Disease Detection in Cayenne Pepper Plantsaper - Ayu Suningsih; Zara Yunizar; Rizki Suwanda (Universitas Malikussaleh, 2026)
Cayenne pepper (Capsicum frutescens L.) is an economically important horticultural crop in Indonesia; however, its productivity is frequently affected by leaf diseases, including leaf curl, leaf spot, and yellow leaf disease. Conventional disease identification mainly relies on visual inspection, making the diagnosis highly dependent on farmers’ experience and increasing the possibility of inaccurate identification and delayed treatment. This study proposes a Convolutional Neural Network (CNN)-based approach for automatic chili leaf disease classification and implements the trained model in an Android application for offline real-time detection. A total of 2,000 images were collected from chili plantations located in Lhokseumawe City and Aceh Tamiang Regency. The dataset was organized into five balanced categories, namely healthy leaf, leaf curl, leaf spot, yellow leaf, and non-leaf, with 400 images assigned to each category. The addition of a non-leaf category enables the application to distinguish chili leaves from irrelevant objects during mobile-based detection. Before training, all images were resized to 150 × 150 pixels, normalized, and partitioned into training, validation, and testing sets using an 80:10:10 ratio. The proposed CNN architecture comprised three convolutional layers followed by max-pooling layers, a flatten layer, a fully connected layer, a dropout layer, and a Softmax output layer. Experimental evaluation on the testing dataset produced an overall accuracy of 89.50%, while the macro-average precision, recall, and F1-score reached 90%, 89%, and 89%, respectively. The trained model was successfully converted into TensorFlow Lite (TFLite) format and integrated into an Android application capable of providing real-time disease prediction, confidence scores, disease descriptions, treatment recommendations, and detection history without requiring an Internet connection. These results indicate that the proposed system is suitable for practical field deployment to support early identification of chili leaf diseases.