Smart Management of Crop Monitoring Using Drone Technology

Agriculture is a critical industry facing challenges such as early disease detection, inefficient pest control, and excessive chemical pesticide use, which lead to significant crop yield losses. To address these issues, we propose a smart crop monitoring system that leverages AI, IoT, and drone technology to provide real-time disease detection and geo-tracking of affected plants. Proposed system integrates Raspberry Pi, GPS, and deep learning models to automate disease identification and pesticide recommendations, with a strong emphasis on organic farming practices. The core functionality of Proposed system is built on a hybrid deep learning architecture. A TensorFlow Lite model, deployed on Raspberry Pi, performs on-device disease classification using images captured by a camera module along with the plant's geo-location. When local model fails to classify an image, the data is transmitted to a cloud-based TensorFlow model, where advanced image preprocessing and disease classification take place. The identified disease, along with its location and recommended treatments, is stored in MongoDB. Over time, the cloud model continuously updates the database, improving future predictions. A farmer-friendly mobile application retrieves data via APIs, allowing users to monitor real-time disease alerts, affected plant locations, and pesticide recommendations, prioritizing organic solutions while also suggesting chemical alternatives. Experimental results demonstrate high efficiency, with our deep learning model achieving an accuracy of 98% and a minimal loss of 2.5 %, ensuring precise disease classification and reliable predictions. This intelligent system provides modernizing agriculture and enhancing crop health management.