RuralAI in Tomato Farming: Integrated Sensor System, Distributed Computing, and Hierarchical Federated Learning for Crop Health Monitoring
Harish Devaraj, Shaleeza Sohail, Melanie Po‐Leen Ooi, Boyang Li, Nathaniel Hudson, Matt Baughman, Kyle Chard, Ryan Chard, Enrico Casella, Ian Foster, Omer Rana
Abstract
Precision horticulture is evolving due to scalable sensor deployment and machine learning integration. These advancements boost the operational efficiency of individual farms, balancing the benefits of analytics with autonomy requirements. However, given concerns that affect wide geographic regions (e.g., climate change), there is a need to apply models that span farms. Federated Learning (FL) has emerged as a potential solution. FL enables decentralized machine learning (ML) across different farms without sharing private data. Traditional FL assumes simple 2-tier network topologies and thus falls short of operating on more complex networks found in real-world agricultural scenarios. Networks vary across crops and farms, and encompass various sensor data modes, extending across jurisdictions. New hierarchical FL (HFL) approaches are needed for more efficient and context-sensitive model sharing, accommodating regulations across multiple jurisdictions. We present the RuralAI architecture deployment for tomato crop monitoring, featuring sensor field units for soil, crop, and weather data collection. HFL with personalization is used to offer localized and adaptive insights. Model management, aggregation, and transfers are facilitated via a flexible approach, enabling seamless communication between local devices, edge nodes, and the cloud.