Web-Based Diagnostic Tool for Pulse Crop Disease Identification Using Convolutional Neural Networks

Plant diseases threaten food security, especially in pulse crops essential for sustainable agriculture. This study explores Convolutional Neural Networks (CNNs) for rapid detection of major diseases such as Ascochyta blight, Fusarium wilt, and phytoplasma infections, which cause severe losses in countries like Ethiopia, India, and Taiwan. Using advanced imaging, we propose a web-based diagnostic tool to empower farmers with timely, accurate disease identification, enabling effective management decisions. This approach offers not just technological advancement but also economic sustainability for smallholder farmers by improving crop health and yields through early intervention and precision agriculture practices.