Crop2Cloud Platform: Real-Time Data Integration for Agricultural Water Monitoring

Authors

Bryan Nsoh, University of Nebraska-LincolnFollow
Abia Katimbo, University of Nebraska-LincolnFollow
Kendall DeJonge, United States Department of Agriculture, Agricultural Research Service, Water Management and Systems Research UnitFollow
Wei-zhen Liang, University of Nebraska-LincolnFollow
Hongzhi N. Guo, University of Nebraska-LincolnFollow
Yufeng Ge, University of Nebraska-LincolnFollow
Derek M. Heeren, University of Nebraska-LincolnFollow
Yeyin Shi, University of Nebraska-LincolnFollow
Xin Qiao, University of Nebraska-LincolnFollow
Daran R. Rudnick, Kansas State University
Hope Njuki Nakabuye, Texas A & M University
Birru Girma, United States Department of Agriculture, Agricultural Research Service, Agroecosystem Management Research UnitFollow
Isa Kabenge, Makerere University
Joshua Wanyama, Makerere University

ORCID IDs

Nsoh https://orcid.org/0009-0003-1495-6281

Katimbo https://orcid.org/0000-0001-8615-9925

DeJonge https://orcid.org/0000-0003-3683-4149

Heeren https://orcid.org/0000-0002-0222-5516

Nakabuye https://orcid.org/0000-0002-5087-2830

Wanyama https://orcid.org/0000-0003-1799-4788

Document Type

Article

Date of this Version

2025

Citation

Smart Agricultural Technology (2025) 12: 101166

doi: 10.1016/j.atech.2025.101166

Comments

United States government work

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 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 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.