Birds and Human Detection System for Crop Field’s Breach Surveillance Using Single Shot MultiBox Detector (SSD) Algorithm

Birds and human intrusions pose a significant threat to crop fields, leading to loss of yield and revenue for farmers. It is necessary to detect and alert farmers of potential breaches in real-time to mitigate the risk in the surveillance system. In this study, we developed a Bird and Human Detection System for Crop Field Breach Surveillance using Single Shot MultiBox Detector (SSD) Algorithm. Using the SSD algorithm, a cutting-edge object detection method, the suggested system analyzed surveillance photos to identify and categorize humans and birds in crop fields. The system achieved a high accuracy of 89.78%, with an average precision of 73.85% for human detection and 88% for bird detection. The proposed approach provides an effective solution for crop field surveillance, enabling farmers to take prompt action to prevent crop losses.