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

Authors: NSOH, BRYAN - University Of Nebraska; KATIMBO, ABIA - University Of Nebraska; DeJonge, Kendall; LIANG, WEIZHEN - University Of Nebraska; GUO, HONGZHI - University Of Nebraska; GE, YUFENG - University Of Nebraska; HEEREN, DEREK - University Of Nebraska; SHI, YEYIN - University Of Nebraska; QIAO, XIN - University Of Nebraska; RUDNICK, DARAN - Kansas State University; NAKABUYE, HOPE - Texas A&M Agrilife; Birru, Girma; KABENGE, ISA - Makerere University; WANYAMA, JOSHUA - Makerere University

Submitted to: Smart Agricultural Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/3/2025
Publication Date: 7/4/2025
Citation: Nsoh, B., Katimbo, A., DeJonge, K.C., Liang, W., Guo, H., Ge, Y., Heeren, D., Shi, Y., Qiao, X., Rudnick, D., Nakabuye, H., Birru, G.A., Kabenge, I., Wanyama, J. 2025. Crop2Cloud platform: Real-time data integration for agricultural water monitoring. Smart Agricultural Technology. 12. Article e101166. https://doi.org/10.1016/j.atech.2025.101166.
DOI: https://doi.org/10.1016/j.atech.2025.101166

Interpretive Summary: Water is crucial for farming, but it's hard to know exactly when and how much to water crops. To solve this problem, scientists created the Crop2Cloud (C2C) platform. This system uses advanced sensors to track soil moisture, plant temperature, and weather. It analyzes this data to give farmers real-time advice on when to water. The C2C platform helps farmers use water more efficiently. However, it faces challenges like sensor damage and power issues. Despite these challenges, the C2C platform can help conserve water by optimizing irrigation, and future improvements aim to make it even better at saving water and energy.

Technical Abstract: 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 (Tc) measured by Infrared Radiometers (IRTs) (Apogee Instruments, US), as well as weather information and estimated Crop Evapotranspiration (ETc) from FAO56 approach. Computed WSIs included Jackson’s 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 ETc. 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.