Crop2Cloud platform: Real-time data integration for agricultural water monitoring

• Crop2Cloud platform uses multiple data sources for irrigation scheduling. • Advanced and common irrigation methods are integrated into the platform. • The platform uses real-time data from the fields for actionable irrigation decisions. • Crop2Cloud platform was tested and performed well while irrigating corn. • Challenges faced were data gaps in the season due to sensor damage and power breakouts. Efficient water management is vital for sustainable agriculture, yet integrating real-time data for precise irrigation remains a challenge. This study designed the Crop2Cloud (C2C) platform, a system that leverages advanced sensors using Internet of Things (IoT), edge and cloud computing techniques, and computed Water Stress Indices (WSIs) and machine learning models (i.e., fuzzy logic), to provide scalable and real-time irrigation decisions. The C2C platform aggregates several data including Volumetric Water Content (VWC) from TDR sensors (Acclima Inc., US) installed at four multiple depths, canopy temperatures (T c ) measured by Infrared Radiometers (IRTs) (Apogee Instruments, US), as well as weather information and estimated Crop Evapotranspiration (ET c ) from FAO56 approach. Computed WSIs included the theoretical Crop Water Stress Index (CWSI) and Soil Water Stress Index (SWSI) as a ratio of Volumetric Water Content (VWC), measured and that at Field Capacity (FC) and Maximum Allowable Depletion (MAD). Additionally, fuzzy-logic irrigation schedule was developed using different fuzzy rules and three available water use indicators – CWSI, SWSI, and ET c . A designed dashboard can display collected data, computed WSIs, and irrigation recommendations from selected methods: only CWSI, only SWSI, combining SWSI + CWSI, and fuzzy logic. The C2C platform can provide quick and real-time crop performance insights and data-driven decisions for timely water application. However, there are logistical challenges such as sensor damage and power management which impact the platform’s performance and efficiency. Future work will involve refining the system to avoid data gaps and improving scheduling methods to optimize irrigation applications to increase water and energy savings.