Advancing Smart Farming with UAV-Based Deep Learning for Proactive Wheat Disease Management

Wheat diseases are a huge risk to the food security of the world and can result in extreme yield loss in case they are not diagnosed and treated at an early stage. Previously, disease identification required manual scanning, which is labor intensive, time-consuming, and inaccurate. The project shows a real-time, scalable, and automated wheat disease detection system with Unmanned Aerial Vehicles (UAVs), multispectral sensing, and deep learning. The system described above is unlike the conventional ground-based methods, the model uses aerial imagery collected using UAVs in conjunction with deep Convolutional Neural Networks (CNNs), specifically fine-tuned VGG19, to enhance scalability and accuracy. Grad-CAM visualization is also integrated to make predictions from models interpretable by farmers. In addition, the system incorporates real-time processing as well as IoT for automated early disease intervention alerting. The combination of large-scale monitoring, high-accuracy disease detection, and farmer-friendly deployment, this project fills important gaps in existing research with a pioneering, field-application solution for precision agriculture. Experimental results show high detection accuracy, proving its robustness under varying environmental conditions. This technology allows farmers to obtain real-time information, enabling timely disease management and sustainable wheat production.