Tree Leaves Based Disease Prediction and Fertilizer Recommendation Using Deep Learning Algorithm

Abstract

Tree health is critical for maintaining ecological balance and sustaining diverse ecosystems. Early detection of diseases affecting tree leaves can aid in timely intervention and mitigation efforts. This paper proposes a novel approach to tree disease prediction based on deep learning, specifically the VGG16 convolutional neural network architecture and focuses on analyzing high-resolution images of tree leaves to determine whether they are healthy or infected with a specific disease. The methodology entails gathering a large dataset of images of tree leaves from various species and disease types. To improve the model's robustness and generalization, data preprocessing techniques such as image resizing, normalization, and augmentation are used. For feature extraction, the pre-trained VGG16 model is used, and the top layers are tailored to the tree disease prediction task. To improve its performance, the proposed model goes through rigorous training and validation processes. To assess the model's effectiveness in disease classification, metrics such as accuracy, precision, recall, and F1 score are used. The study's goal is to develop a dependable and efficient tool for arborists, foresters, and environmentalists to quickly identify and treat tree diseases. The findings of this paper provide advance precision agriculture and environmental monitoring by providing a scalable and automated solution for early tree disease detection. Furthermore, the paper investigates potential applications in real-world scenarios, fostering sustainable practices for global ecosystem preservation.