Smart Agriculture: Enhancing Security Through Animal Detection Via Deep Learning and Computer Vision

Abstract

Agriculture stands as a crucial sector, making significant contributions to the economies of many countries. Nevertheless, it encounters various challenges, one of which is animal disruption. This poses a considerable threat to crops, leading to financial losses for farmers. In response to this concern, we have engineered an animal disruption warning system for agricultural settings based on YOLOv6 technology.The system operates by analyzing live video feeds from strategically placed cameras. Utilizing deep learning algorithms, it can detect and classify animals in real-time. The computer vision algorithms enable tracking and prediction of animal movements. Upon detection, the system promptly sends alerts, enabling timely and appropriate actions.In this paper, we periodically monitor the entire farm through a camera that continuously records its surroundings. The identification of animal entry is achieved using a deep learning model, and alarm systems serve as a deterrent, notifying forest officials. This report provides details on the libraries and convolutional neural networks employed in constructing the model.This research focuses on the implementation of a robust animal detection system in agricultural environments, leveraging the capabilities of deep learning. The project utilizes state-of-the-art deep neural networks and computer vision algorithms to analyze live video feeds from strategically positioned cameras across the farm. The deep learning model is trained to detect and classify various animals in real-time, contributing to the early identification of potential threats to crops.The system employs sophisticated computer vision techniques, enabling accurate tracking and prediction of animal movements within the monitored areas. Upon detection, the system triggers timely alerts, providing farmers with the necessary information to take swift and appropriate actions, thereby mitigating potential damage to crops.To achieve these objectives, the project involves periodic monitoring of the entire farm through a camera that continuously records its surroundings. The deep learning model, supported by alarm systems, effectively identifies animal entries, serving as a proactive deterrent. This research report outlines the libraries, frameworks, and convolutional neural networks employed in the development of the animal detection model, shedding light on the technical aspects of its implementation.The integration of deep learning and computer vision in agriculture not only enhances crop protection but also contributes to the sustainable and efficient management of farming practices. This research offers insights into the potential of advanced technologies to address challenges in agriculture and opens avenues for further exploration in the intersection of technology and agriculture.