Adapting Deep-Learning in Early Yam Disease Detection Using Lenet-5 (Adam Optimizer) Convolutional Neural Network Architecture to Improve Productivity and Enhance Farmers Social Habits in the Digital Age

The agriculture sector faces significant challenges due to diseases affecting crop yields, particularly in yam cultivation. This study explores the adaptation of deep learning techniques for early detection of yam diseases using a LeNet-5 Convolutional Neural Network (CNN) architecture optimized with the Adam optimizer. The fam sides considered are; Ardokola, Zing and Mutum Biu in Taraba State, Nigeria. By leveraging advanced image processing and machine learning methodologies, we aim to develop an effective diagnostic tool that empowers farmers to identify and manage diseases promptly, ultimately improving productivity. This research not only enhances the technological capabilities of farmers in the digital age but also promotes better agricultural practices, fostering social habits that encourage knowledge sharing and community engagement. The proposed system is tested on a comprehensive dataset of yam leaf images, demonstrating its ability to accurately detect various disease conditions at 17.84%. Results indicate a significant improvement in recognition accuracy, suggesting that the integration of AI-driven solutions can transform disease management approaches in yam farming, contributing to sustainable agricultural practices and improved livelihoods for farmers.