Danung Nur Adli, Tirana Noor Fatyanosa, Fais Al Huda, Mohammad Miftakhus Sholikin, Sugiharto Sugiharto
The growing global demand for broiler meat has underscored the importance of precision livestock farming to increase productivity, animal welfare, and sustainability. This study integrated Internet of Things (IoT) sensor networks with the extreme gradient boosting (XGBoost) algorithm to model how environmental factors—particularly temperature and humidity—affect broiler chickens’ performance indicators, including feed intake, body weight, and the feed conversion ratio (FCR). A total of 160 unsexed MB-Lohmann broiler chickens were raised under controlled conditions via a plant-based diet free of antibiotic growth promoters. Real-time data were collected via custom-built IoT sensor nodes, which generated over 19,000 environmental readings across a 35-day trial. Weekly growth performance measurements were collected and analysed via both statistical and machine learning approaches. Feed intake and weight gain were strongly positively correlated (r = 0.89), whereas FCR was strongly negatively correlated with feed efficiency (r = –0.95). Humidity was moderately associated with reduced feed efficiency (r = –0.82), suggesting possible environmental stress. Among the machine learning models tested, the multilayer perceptron (MLP) demonstrated the most consistent and accurate performance in predicting growth outcomes. In summary, this work highlights the potential of combining IoT technology with advanced analytics to support real-time decision-making in broiler production. By enabling more responsive and informed farm management, such approaches could play a vital role in improving efficiency, reducing mortality, and supporting sustainable poultry systems. © 2025 The Authors
Department of Feed and Animal Nutrition, Smart Livestock Industry Study Programme, Faculty of Animal Science, Universitas Brawijaya, East Java, Malang, Indonesia; Department of Informatics Engineering, Faculty of Computer Science, Universitas Brawijaya, Malang, Indonesia; Research Centre for Animal Husbandry, National Research and Innovation Agency (BRIN), Bogor, Indonesia; Department of Animal Science, Faculty of Animal and Agricultural Sciences, Universitas Diponegoro, Tembalang Campus, Semarang, Central Java, Indonesia
