RiceShield: AI–IoT-Based Early Pest and Spoilage Prediction for Grain Storage

Abstract

Post-harvest grain storage often suffers significant losses from unnoticed pest infestations and spoilage. These issues usually start internally and go undetected until serious damage happens. To tackle this problem, we introduce RiceShield, a smart grain protection system using AI and IoT technology. This system predicts and identifies early signs of infestations in stored rice. RiceShield monitors important environmental factors like gas concentration, temperature, humidity, and monitoring by Camera controlled by an ESP32 platform. An increase in carbon dioxide levels is a warning sign of pest activity, while ammonia presence points to spoilage caused by microbial growth. By continuously tracking these conditions and sending timely alerts, the system allows for early action before visible damage occurs. This approach provides a non-invasive, cost- effective, and scalable solution that improves grain safety, reduces post-harvest losses, and supports sustainable agricultural storage methods.