Automated Real-Time Infection Detection in Oyster Mushroom Using Smart Farming System

In mushroom cultivation, particularly with oyster mushrooms, the presence of diseases like Trichoderma can lead to significant losses in yield and quality affecting an average of an 11% total production is affected in average cultivation. Manual monitoring and detection of such infections are labor-intensive and prone to human error. Additionally, since the infection is fungal and spreads quickly through common mediums including water, air, and humans, human surveillance leads to its continued spread. This paper aims to address these challenges by implementing an innovative solution that leverages computer vision, image processing, and IoT technologies. By minimizing human involvement, the system significantly diminishes the threat of infection spread through human contact and breathing, reducing it by 95%. It addresses the chance of human error in infection detection and optimizes the monitoring tasks. Overall, this automated solution promises an impressive 11% production boost. The proposed automated solution is evaluated with curated dataset and we are able to achieve 94% of accuracy.