Mobilenet-based Transfer Learning for Detection of Eucalyptus Pellita Diseases - Deviana Sely Wita; Agus Subekti (Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI, 2023)
Currently, the pulp industry in Indonesia is ranked eighth in the world and the paper industry is ranked sixth in the world. One of the advantages in supporting the industry is that Indonesia has a large Industrial Plantation Forest (HTI) where the plants for pulp and paper raw materials originate. Eucalyptus pellita species belonging to the Myrtaceae family is one of the priority species for Industrial Plantation Forests (HTI) because of its adaptability and its wood can be used as raw material for pulp. Industrial Plantation Forests of this type can be found mainly in Kalimantan and Sumatra. This species shows good growth in stem shape, growth speed and good wood quality and has high germination and has a shorter cutting cycle of about 7-8 years so that it is quickly harvested. Prevention and treatment of leaf disease is one of the main processes of planting. Early diagnosis and accurate recognition of Eucalyptus Pellita disease can control the spread of the disease and reduce production costs and treatment costs. Disease detection on Eucalyptus pellita leaves can be done automatically faster by utilizing digital image processing and artificial intelligence. In this study, we propose a detection method with Deep Learning architecture. Our proposed method is based on pre-trained transfer learning using MobileNet. Image datasets from PT. Surya Hutani Jaya's land in East Kalimantan were used to train the model. The dataset is divided into three classes where 1 class is healthy leaves and 2 classes are sick leaves, namely Xanthomonas Bacteria and Cylindrocladium Fungi. With a dataset ratio of 70: 20: 10 the number of training datasets is 2370, validation is 591, and Testing is 177. Hyperparameter scenarios were carried out on the MobileNet model to optimize performance on the Eucalyptus Pellita leaf dataset. The experimental results show a fairly good accuracy, reaching 98%.