Reliability-aware federated ordinal learning for weekly irrigation management zone prediction on edge nodes

Publication Type

Journal Article

Name of Author

• Rehema Mwawado, African Centre of Excellence in Internet of Things, University of Rwanda, KN 7 Ave, Kigali, P.O. Box 3900, RwandaFollow
• Jimmy Nsenga, African Centre of Excellence in Internet of Things, University of Rwanda, KN 7 Ave, Kigali, P.O. Box 3900, Rwanda
• Marco Zennaro, Telecommunications/ICT4D Laboratory, Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy
• Damien Hanyurwimfura, African Centre of Excellence in Internet of Things, University of Rwanda, KN 7 Ave, Kigali, P.O. Box 3900, Rwanda
• Antonios Morellos, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Ghent, East Flanders, 9000, Belgium
• Muhammad Qaswar, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Ghent, East Flanders, 9000, Belgium
• Abdul Mounem Mouazen, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Ghent, East Flanders, 9000, Belgium

Publication Date (Issue Year)

2026

Journal Name

Smart Agricultural Technology

Abstract

The vision of smart agriculture depends on systems that can autonomously integrate distributed sensor data to support on-farm decisions such as precision irrigation and nutrient management. In variable-rate irrigation (VRI), this requires timely, high-resolution information to generate management zones that adapt to within-field variability throughout the season. However, current practice often relies on static zone maps and centralized processing, both of which are poorly suited to dynamic field conditions where sensors may fail, or drift and internet connectivity is often limited in resource-constrained environments. This study presents a reliability-aware edge-learning pipeline for weekly VRI zone prediction using federated learning (FL). The proposed framework integrates in-field soil-moisture sensing, weather aggregates, and crop normalized difference vegetation index (NDVI) indicators within an FL setting, enabling field nodes to collaboratively train a shared model without exchanging raw data. This reduces communication demands while preserving data locality. To address sensor unreliability, we develop a reliability model that quantifies weekly data completeness, quality, and stability. These reliability estimates are incorporated through a dual-weighting mechanism: unreliable samples are down-weighted during local training, while more trustworthy clients are given greater influence during global aggregation. The pipeline further employs a rank-consistent ordinal regression (CORAL)-based ordinal classifier to align predictions with ordered irrigation categories: Low, Medium, Medium-High, and High. Experimental evaluation under deployment-oriented operation achieved strong predictive performance, with an accuracy of 0.94, a macro-F1 score of 0.90, and a quadratic weighted kappa (QWK) of 0.98 at the best checkpoint. Multi-week spatial comparisons further showed strong agreement between predicted and geographic information system (GIS)-derived reference zones, while forward-chaining evaluation confirmed useful temporal robustness, with mean accuracy, macro-F1, and QWK values of 0.80, 0.58, and 0.78, respectively. Overall, the proposed framework provides a robust and communication-efficient approach to weekly irrigation zoning that is suitable for farm-scale deployment under unreliable sensing and constrained connectivity.

Keywords

Federated learning, Edge computing, Variable-rate irrigation, Precision agriculture, Soil moisture, Sensor reliability, Ordinal classification

Rsif Scholar Name

Rehema Hamis Mwawado

Rsif Scholar Nationality

Tanzania

Cohort

Cohort 4

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Africa Host University (AHU)

University of Rwanda (UR), Rwanda

Recommended Citation

Mwawado, R., Nsenga, J., Zennaro, M., Hanyurwimfura, D., Morellos, A., Qaswar, M., & Mouazen, A. M. (2026). Reliability-aware federated ordinal learning for weekly irrigation management zone prediction on edge nodes. Smart Agricultural Technology, 15 https://doi.org/10.1016/j.atech.2026.102460