GLS-Based Calibration of Low-Cost IoT Sensors in Poultry Houses
DOI:
https://doi.org/10.36557/2674-9432.2026v5n2p772-786Keywords:
environmental monitoring, GLS regression, IoT sensor calibration, precision livestock farming, smart agricultureAbstract
This paper presents a statistical calibration methodology for low-cost IoT temperature and humidity sensors applied to environmental monitoring in poultry houses. Addresses the need for reliable microclimatic data in intensive poultry production, where low-cost sensors are prone to systematic bias and correlated errors. The objective is to assess whether Generalized Least Squares (GLS) regression can improve measurement accuracy under real operating conditions. Two DHT22 sensors were evaluated against a NIST-traceable reference using synchronized indoor and outdoor measurements. GLS-based calibration models were developed to account for temporal dependence and variance heterogeneity, and performance was assessed using standard accuracy metrics. After calibration, temperature measurements achieved near-reference accuracy (R² = 1.0, MAE = 0.108 °C), while relative humidity showed strong correlation with the reference (R² = 0.96–0.985) but retained residual bias at extreme ranges. The results confirm the effectiveness of GLS-based calibration for low-cost environmental sensors and highlight the need for further refinement in humidity measurements
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Copyright (c) 2026 Igor Dias Modesto, Leandro Ataide Barbosa de Oliveira, Diego Fernandes Goncalves Martins, Jackson Gomes Soares Souza, Danilo Douradinho Fernandes, Glauber da Rocha Balthazar
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