Rossy Nurhasanah, Fikri Fadhlillah, Niskarto Zendrato, Melly Br Bangun
This study aims to develop a mobile-based deep learning system for precise Amorphophallus muelleri Blume leaf disease detection and classification. The diseases, primarily caused by fungi, nematodes, and larvae, often exhibit similar leaf symptoms. By implementing the SSD-MobileNet method, our proposed system achieves the highest accuracy rate of 95.45% with the farthest distance that can be detected by the system is 15 cm in real-time classification. Additionally, through image resizing to 640x640 pixels and fine-tuning parameters, including epochs, momentum, and learning rates, our approach demonstrates high-performance suitability for mobile platforms. © 2023 IEEE.
Universitas Sumatera Utara, Department of Information Technology, Medan, Indonesia; Universitas Negeri Medan, Department of Non Formal Education, Medan, Indonesia
