TDC: An MLP-based Sustainable DL Model for Oak Wilt Disease Classification

Trees conservation and protection have been an active and significant domain of research and concern for the betterment of the globe. Tree diseases and infection remain an area to explore by various researchers, therefore an efficient and sustainable Tree Disease Classification (TDC) model has been developed with the help of Deep Learning (DL) based Multi-Layer Perceptron (MLP) approach. The complete implementation has been conducted on a real-time oak_wilt 1.0 dataset consisting of 4000 images resulting in an identification outcome of 94.53% accuracy and best classification accuracy of 99.3% under level 2 of oak wilt disease. The outcomes of the proposed work have proved the efficiency and sustainability of the TDC model for the identification and classification of the oak wilt tree disease. The proposed work contributes to nature conservation, environment upgradation, and building better environmental economic, and sustainable development for both people and researchers in the discussed domain.