Identification of Mangrove Leaf Disease in Deep Convolutional Neural Network Approach

Mangrove forests in Indonesia are distributed over many coastal regions, exhibiting significant biodiversity and diverse structural forms. Mangroves serve as a mitigating factor against wave action and intense wind energy resulting from adverse weather conditions. Another purpose of mangroves is to provide as a food source for various marine species. Mangroves consist of many principal components, including roots, stems, leaves, and blooms. For optimal growth, some portions of mangrove plants must be safeguarded from several diseases, whether induced by parasites/pests or by environmental conditions. The leaves of the mangrove are the most vulnerable to disease; yet, pests visible on the leaves are typically inadequately managed, resulting in the plant being immediately removed when all its leaves are infested. This study uses a Deep Convolutional Neural Network to recognize these pests effectively. The image processing methods used in this study include smoothing and sharpening. The study findings indicate that the used approach effectively identifies mangrove leaf diseases, with an accuracy of 99.2%.