Dwi Ahmad Dzulhijjah, Kusrini Kusrini, Afdhal Afdhal, Noor Akhmad Setiawan, Adi Wibowo
Kerangas forests (tropical heath forests) are nutrient-poor, fire-prone ecosystems threatened by deforestation and land-use change. Monitoring these post-disturbance landscapes is crucial for ecological restoration but remains challenging due to field inaccessibility and the high resource demands of conventional deep learning. This study evaluates lightweight CNNs (MobileNetV1/V2) for classifying Kerangas imagery into three ecological succession stages. Using transfer learning and domain-specific data, twelve RGB and grayscale models were assessed by accuracy, latency, RAM, and flash usage for edge deployment. MobileNetV2 with 160×160 RGB and α =0.75 reached 95.8% accuracy, matched by a 96×96 grayscale model with α =0.35 - which reduced memory by over 97% and achieved sub-second inference time. © 2025 IEEE.
Institut Teknologi Indonesia, Program Profesi Insinyur, Tangerang, Indonesia; Universitas AMIKOM, Computer Science Faculty, Yogyakarta, Indonesia; Politeknik Elektronika Negeri Surabaya, Department of Informatics and Computer Engineering, Indonesia; Universitas Syiah Kuala, Department of Electrical and Computer Engineering, Banda Aceh, Indonesia; Universitas Gadjah Mada, Department of Electrical and Information Engineering, Yogyakarta, Indonesia; Universitas Diponegoro, Faculty of Science and Mathematics, Department of Informatics, Semarang, Indonesia
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