Unified Pest Prevention and Control System Based on AIoT for Sustainable Agriculture

Traditional agriculture often relies heavily on pesticides for distributed pest management, which increases production costs and can lead to environmental pollution. The inefficiency and environmental impact of these methods highlight the need for more sustainable solutions. Artificial Intelligence Internet of Things (AIoT) enables precise monitoring and prediction of pest infestations in agriculture, facilitating targeted interventions to effectively reduce crop loss rates. This paper proposes a unified pest prevention and control system based on Agricultural Social Internet of Things (Agri-Social IoT) and artificial intelligence algorithm. By social relationships among devices, the system aims to enhance agricultural production efficiency through unified pest prediction and prevention. To achieve rapid and accurate detection of plant pests, this paper introduces an innovative detection method named Ghost-YOLO-ShuffleAttention (GhostYOLOSA). This artificial intelligence method improves the capture of spatial relationships and contextual information, enhancing the detection accuracy for small targets. It significantly reduces the computational load and model parameters while ensuring high detection accuracy, making it suitable for resource-limited devices. Experimental results from datasets and real-world applications demonstrate that the unified pest control system performs well in pest detection and prevention tasks, promoting sustainable agricultural development.