| Abstrakt: |
Indonesia is the largest supplier of patchouli oil in the world market, contributing 80%-90%. Most patchouli oil products are exported in the perfume, cosmetics, pharmaceutical, antiseptic, aromatherapy, and insecticide industries. The emergence of patchouli leaf disease significantly reduced the production of wet, dry, oil, and patchouli alcohol. Therefore, selecting patchouli cuttings (seedlings) that are entirely healthy and disease-free is very important to prevent disease transmission from one area to another. In addition, the selection of disease-free seeds is also essential to prevent the use of diseased patchouli plant propagation. So far, the early identification of patchouli plant health is carried out through visual observations by experts using antiviral serum tested in the laboratory. However, this testing process is expensive. Therefore, in this paper, we proposed a novel Convolutional Neural Network (CNN) architecture for patchouli leaf diseases. We proposed a system for early identification of whether a patchouli leaf is diseased or healthy. Our CNN model uses three convolution layers, a dense layer, and a dropout layer. We compare the proposed model with well-known models, namely EfficientNetB0, AlexNet, InceptionV3, MobileNetV2, and VGG16. The results show that the proposed model outperformed five well-known models as a comparison. It has been confirmed by predicting the new and different testing data. This research contributes to the early identification of patchouli leaf diseases to reduce the expensive costs of identifying patchouli leaf diseases. [ABSTRACT FROM AUTHOR] |
