ANIMAL GAZING DETECTION USING CNN IN SMART AGRICULTURAL FIELD

Abstract [English]

Animal detection and warning systems play a critical role in safeguarding agricultural land from the intrusion of large animals, particularly ungulates, which pose threats to property and human safety. These systems are designed to identify and alert the presence of such animals before they encroach upon farming territories. Across different regions, the deployment of these systems aims to mitigate Animal Vehicle Collisions (AVCs) involving ungulate species, which can result in significant costs and hazards. However, the effectiveness of these systems may be hindered by the lack of regular monitoring for incidents of animal road fatalities. This paper explores the operational principles of animal detection systems, highlighting their reliance on machine learning technology to detect patterns indicative of animal presence. The detection process involves analyzing data collected from various sources to discern behavioral and environmental cues associated with animal activity. Challenges may arise from accurately distinguishing between different types of animals and background elements, necessitating the refinement of machine learning algorithms to minimize false positives triggered by environmental factors. Effective deployment of these systems requires comprehensive planning and integration into regional and national agricultural policies. By leveraging advancements in machine learning technology and data analytics, stakeholders can enhance the efficiency of these systems, thereby reducing the incidence of animal- related accidents and protecting agricultural resources. This paper underscores the importance of continuous monitoring and adaptation of animal detection systems to effectively mitigate risks and ensure the sustained protection of farming land and communities.