KLASIFIKASI JENIS PENYAKIT BUAH MANGGA BERBASIS DEEP LEARNING MENGGUNAKAN ARSITEKTUR RESNET DAN MOBILENET - Nanda Cornelis Rasyid; Joni Karman; Asep Toyib Hidayat; Harma Oktavia Lingga Wijaya (Yayasan Pendidikan Cahaya Budaya Indonesia, 2026)
Mango plantations in Indonesia face significant challenges due to pests and diseases that reduce productivity and cause economic losses for farmers. Manual identification of these issues requires expert knowledge and is often time-consuming and inaccurate. This study aims to develop a classification system for detecting various mango leaf diseases using deep learning models, specifically ResNet and MobileNet architectures. Deep learning, particularly Convolutional Neural Networks (CNNs), enables automatic disease detection from plant images by learning patterns without explicit programming. The proposed system focuses on identifying common diseases such as leaf blight, whiteflies, and leaf caterpillars efficiently and accurately. By leveraging image-based recognition, the system allows for early diagnosis and timely intervention. The results of this research are expected to provide a technological solution that supports modern agriculture and empowers farmers with better disease management tools.