CNN-Based Autonomous Smart Drones for Precision Agriculture and Crop Monitoring

This research examines the utilization of Convolutional Neural Networks (CNNs) in autonomous smart drones for precision agriculture and crop surveillance. The proposed system employs CNN-based image identification to diagnose crop illnesses, detect pests, monitor growth patterns, and evaluate soil health in real-time. The intelligent drones, fitted with high-resolution cameras, capture aerial imagery of crops, which is subsequently analyzed using CNN for accurate data extraction. In field trials, the system attained an accuracy rate of 92% for crop disease detection, 85% for pest identification, and 89% for soil quality assessment. Moreover, the drones diminished the duration of agricultural inspection by 60% relative to conventional manual techniques. The results achieved a 30% enhancement in total crop production through the optimization of irrigation and fertilizer application informed by real-time data, while simultaneously decreasing pesticide usage by 25%, so fostering environmentally sustainable practices. This CNN-based autonomous drone system enhances agricultural efficiency, sustainability, and resource management, presenting a scalable solution for contemporary farming.