DESIGN AND DEVELOPMENT OF AI-BASED FRAMEWORK FOR PLANT DISEASE CLASSIFICATION

Abstract

Agriculture plays a pivotal role in economic growth and food security, with over 60% of India’s population relying on it for livelihood. However, crop production faces ongoing obstacles, including pest invasions, environmental variability, and plant leaf diseases, which can severely impact output and quality. Manual disease diagnosis takes time and money and is frequently out of reach for farmers in remote areas. This requires the development of intelligent, automated illness-detection technologies that are both timely and accurate. This thesis provides a complete framework that combines Artificial Intelligence (AI), Deep Learning (DL), and the Internet of Things (IoT) to identify and classify plant diseases. The work begins with a thorough literature review and a taxonomy of AI and IoT-based tech niques to identify key research needs. It then explores transfer learning-based models and the Deep Transfer Learning and Extreme Learning Machine (VESTACK) framework, fol lowed by attention-driven deep learning models such as Agriculture Transfer Learning Vision Transformer (AgriTL-ViT) and Asymmetric and Dilated Convolution ResNet (ADResNet) with Attention, which improve feature extraction and classification efficiency. Furthermore, a Federated Learning (FL) technique based on Convolutional Neural Networks (CNNs) and Transformer encoders is created to enable decentralized, privacy-preserving plant disease diagnostics in real-world agricultural environments. The suggested methodologies show consistent performance across diverse plant leaf datasets, demonstrating the promise of AI-driven solutions for real-time, scalable, and smart agricul ture. This thesis advances precision farming by providing a pathway to early disease iden tification, reduced crop losses, and enhanced agricultural sustainability. Future directions include expanding the framework for multi-disease identification, integrating cross-device FL, and deploying real-world IoT systems to improve smart agriculture operations.