Leveraging Intel Developer Cloud: Leaf Disease Detection using CNN

This research paper presents a novel approach to improving leaf disease detection performance by integrating Convolutional Neural Networks (CNNs) and optimization techniques utilizing the Intel OneAPI toolkit. The study focuses on enhancing key performance metrics including accuracy, loss function, and computing time, with the ultimate goal of deploying the system on the Intel Developer Cloud. Leveraging CNNs for image classification tasks and Intel’s advanced toolkit, the methodology explores various optimization strategies to achieve superior results. Results demonstrate significant enhancements in accuracy, reduction in loss function, and optimization of computing time, showcasing the effectiveness of the proposed approach. This research contributes to the advancement of agricultural technology by providing a robust and efficient solution for detecting leaf diseases, with implications for sustainable crop management practices.