Published Paper Details:

Agriculture is the backbone of food security and economic stability, but crop diseases significantly impact yield and quality. Traditional disease detection methods often require expert intervention, making them time-consuming and inaccessible to many farmers. To address this challenge, this research proposes an Augmented Reality (AR)-Driven Disease Detection System for smarter farming. The system integrates computer vision, deep learning, and AR technology to provide real-time disease identification and treatment recommendations. The proposed framework utilizes image processing and convolutional neural networks (CNNs) to detect plant diseases from images captured by AR-enabled devices such as smartphones. The processed results are overlaid on the plant using AR visualization, allowing farmers to recognize affected areas and access treatment solutions instantly. Additionally, the system provides real-time recommendations, preventive measures, and expert consultation options, ensuring a proactive approach to disease management. By leveraging machine learning, real-time data processing, and AR-based visualization, this solution enhances precision farming, reduces dependency on chemical treatments, and improves crop health monitoring efficiency. Farmers receive immediate, visual feedback on their crops, highlighting potential disease symptoms through AR interfaces. Furthermore, the system facilitates teleconsulting features, enabling farmers to seek expert advice remotely. This approach not only reduces crop losses but also promotes sustainable agricultural practices by minimizing excessive chemical use and optimizing disease management strategies. Implementing this AI-driven smart farming technology aims to empower farmers, enhance decision-making, and contribute to a more efficient and resilient agricultural sector.