Smart Crop Protection from Animals in Real Time using Convolutional Neural Networks

Crop damage caused by animals such as birds, and wild animals is a significant challenge for farmers worldwide. Traditional methods such as fences, chemical repellents, and scarecrows are often ineffective and can harm the environment and non-target species. To address this challenge, this paper proposes a real-time crop protection system using the most recent object detection technology algorithm, YOLOv7, to address the challenge of crop damage caused by animals such as birds and wild animals. Traditional methods such as fences, chemical repellents, and scarecrows are often ineffective and can cause harm to the environment and non-target species. The suggested project employs a camera to record a live video feed of an agricultural field., which is processed in real-time using YOLOv7 to identify and track animals that are likely to cause damage to crops. The system triggers appropriate actions such as sounding alarms, activating sprinklers, to scare away the animals. This real-time approach can help prevent crop damage and reduce the use of harmful pesticides and other deterrents. The proposed system offers a reliable, cost-effective, and eco-friendly solution to crop protection from animal damage and can be deployed in different crop fields with minimum customization. The proposed system can help farmers to reduce crop damage and improve crop yields, thus contributing to global food security.