Edge-Cloud Infrastructure for Mushroom Growth Monitoring and Analysis

This paper presents the development and implementation of an edge-cloud infrastructure for monitoring and analyzing white and brown button mushroom growth. The system includes edge devices with sensors and cameras for onsite data collection and a cloud platform for data storage and processing. The collected data is processed using a Mask R-CNN model trained on synthetic data to identify and analyze individual mushrooms. Over two years of monitoring, it has demonstrated reliability in capturing and processing data in challenging agricultural environments. These results highlight the potential of integrating advanced machine learning with edge-cloud systems to enhance precision agriculture and resource management.