Scarecrow monitoring system:employing mobilenet ssd for enhanced animal supervision

Abstract

Agriculture faces a growing challenge with wildlife wreaking havoc on crops, threatening sustainability. The project employs advanced object detection, the system utilizes the Mobile Net SSD model for real-time animal classification. The methodology initiates with the creation of a dataset, where each animal is represented by annotated images. The SSD Mobile Net architecture facilitates the use of a model for image classification and object detection. The model undergoes fine-tuning and optimization during training, enhancing accuracy for precise animal classification. Real-time detection is achieved through a webcam and the OpenCV library, enabling prompt identification and categorization of approaching animals. By seamlessly integrating intelligent scarecrow technology with object detection, this system offers a robust solution to field protection, minimizing crop damage and promoting precision farming. It represents a valuable contribution to agricultural sustainability, addressing the challenge of wildlife interference with crops. The implementation of the Intelligent Scarecrow Monitoring System stands as a progressive tool for proactive field management and protection, empowering farmers with an advanced solution for precision agriculture. Keywords: Machine learning, Deep Learning, Computer Vision, MobileNet SSD