AUTOMATED CROP SECURITY: A DEEP LEARNING APPROACH TO DETECTING AND DETERRING BIRDS INTEGRATED WITH A LASER SYSTEM

Abstract

Most people depend on agriculture for their livelihood. Farmers' income is closely linked to crop yield, which has been decreasing due to natural factors and a lack of advanced technology. Birds play a vital role in the ecosystem, but they can also significantly affect crop yield. The birds often damage grain crops, so special attention is needed to address the harm they cause. Controlling the birds is important in farming to prevent the loss of food. In the article, we developed an automatic system to deter birds. The system uses deep learning to detect birds and deter them from crop fields. When a bird enters the farm, the system identifies its location through a picture taken by a camera, and the trained model gives instructions to the laser controller to deter the bird from the crops. This model is trained on the dataset (Bird-feeder). The results show that the trained model performed well, achieving a validation accuracy value of 96.02%, macro F-1 scores of 96.5 %, macro precision of 95.8% and macro recall of 95.4%. The trained model can detect even small birds with accuracy. Farmers can use this model to improve the production of their crops by deterring the birds.

Key Points: Deep Learning; YOLOv11; Automatic Birds Detection and Deterrence; Real Time Object Detection; Agriculture.