Transforming Leaf Disease Diagnosis Through Deep Transfer Learning

Efficient and accurate detection of plant leaf diseases plays a critical role in safeguarding crop yields and ensuring food security. Despite advancements in agricultural technologies, traditional methods for disease identification remain largely manual, time-consuming, and prone to human error. To address these challenges, this proposal outlines a research plan to develop LeafDNet, a deep transfer learning-based framework designed to revolutionize plant disease diagnosis. Leveraging a significantly improved Xception architecture, the proposed model will incorporate additional convolutional and dense layers, augmented with advanced regularization and dropout techniques, to enhance feature extraction and improve classification accuracy. The research will focus on diagnosing common diseases in economically important crops, including roses, mangoes, and tomatoes. A diverse dataset containing over 5000 high-resolution leaf images across multiple disease categories will be used for model training, validation, and testing. Preliminary experimentation suggests that the enhanced architecture can capture complex disease patterns more effectively than existing models. By comparing performance metrics such as accuracy, precision, recall, and F1-score with state-of-the-art methods, we anticipate demonstrating LeafDNet’s superior diagnostic capability. Beyond accuracy improvements, the research will explore scalability, computational efficiency, and real-world deployment potential, including the integration of the model into mobile and IoT devices for real-time field applications. The ultimate goal is to deliver a robust, scalable solution that can empower farmers and agronomists to perform on-the-spot disease diagnosis, contributing to sustainable agricultural practices and reducing crop losses.