Vol. 7, Issue 4, Part D (2025)

Plant diseases could severely compromise agricultural productivity and food security. The current work presents a machine learning-based approach toward plant disease prediction using early and accurate identification of multiple plant diseases employing Convolutional Neural Networks. Model performance has been found using healthy and diseased plant images with data augmentation techniques to avoid over fitting, which provides high accuracy. The proposed system has the capacity for real-time execution: Instantaneous diagnosis is thus given to the farmer using an easy interface. The results therefore depict the utility of CNNs in real-world large-scale agricultural applications and hold great importance as a useful tool for sustainable crop management.