Scarecrow monitoring system:employing mobilenet ssd for enhanced animal supervision

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 ;Comment: 9 pages, 10 figures