Animal incursions pose a significant threat to crop yields, severely affecting agricultural productivity. As agricultural land expands into regions that were previously home to wildlife, human agricultural activities and wild animals’ movement have increased, leading to frequent incidents of crop damage. Farmers are at risk contending not only with pests and environmental challenges but also with the encroachment of animals, all of which contribute to decreased harvests. Conventional methods of crop protection frequently fall short, and employing guards to monitor fields may be impractical. To promote the welfare of both humans and wildlife, it is essential to adopt crop protection strategies that are non-harmful to animals. This research utilizes MobileNet SSD deep learning model, to monitor and detect animals entering agricultural areas. The accuracy achieved was 96.2% on an average and the precision was 94%. By continuously tracking through camera systems, the technology identifies animal intrusions and employs sound-based deterrents to mitigate crop damage.