Deep Learning-Based Plant Leaf Disease Detection in Smart Agriculture

Plant leaf disease detection plays a vital role in precision agriculture. This study proposes a deep learning-based system for automated identification of plant leaf diseases using Convolutional Neural Networks (CNNs). Pre-trained models such as ResNet, InceptionV3, and MobileNet are employed with transfer learning and data augmentation techniques. The system utilizes both public datasets (e.g., PlantVillage) and locally collected field images to ensure robustness across environmental conditions. A mobile application or web-based dashboard is proposed for real-time disease diagnosis, enabling farmers to take timely preventive measures. Integration with IoT-enabled cameras and drones supports large-scale farm monitoring. The system performance is evaluated using accuracy, precision, recall, F1-score, and latency. The proposed framework aims to enhance agricultural productivity through a scalable and accessible smart farming solution