Leaf Disease Detection using Deep Learning and IoT Automation

Agriculture remains a vital pillar for ensuring global food security, yet traditional farming methods often fall short in precision, leading to inefficiencies in resource use, delayed disease detection, and environmental strain. Manual techniques for identifying plant diseases are labor-intensive, costly, and prone to delays that reduce productivity. While existing research has demonstrated the promise of integrating Internet of Things (IoT) and Deep Learning (DL) for enhancing precision agriculture, key limitations such as energy efficiency, real-time monitoring, and dataset diversity persist. This paper proposes a hybrid IoT-DL framework that leverages real-time environmental sensing through IoT-enabled devices, integrated with a Convolutional Neural Network (CNN)-based model for automated and accurate plant disease detection.