Narrowband IoT in Livestock Farming: A Technological Innovation for Productivity and Sustainability

Achmad  Sutanto; Arif Rakhman; Ida  Afriliana; Rudi  Hernowo; Wildani  Eko Nugroho; Mohammad Fayruz

doi:10.46729/ijstm.v5i5.1030

Achmad Sutanto Politeknik Harapan Bersama, Tegal, Indonesia
Arif Rakhman Politeknik Harapan Bersama, Tegal, Indonesia
Ida Afriliana Politeknik Harapan Bersama, Tegal, Indonesia
Rudi Hernowo PT. Anugerah Teknologi Ternak, Semarang Indonesia
Wildani Eko Nugroho Politeknik Harapan Bersama, Tegal, Indonesia
Mohammad Fayruz STIE Ganesha, Ciputat, Indonesia

DOI: https://doi.org/10.46729/ijstm.v5i5.1030

Keywords: Narrowband IoT, precision livestock farming, poultry monitoring, environmental management and anomaly detection.

Abstract

The integration of technology in livestock farming is crucial for enhancing production efficiency and animal welfare. This study aimed to develop and evaluate the implementation of a Narrowband IoT (NB-IoT)-based automated monitoring system in poultry farming. Using an experimental design, the research involved 30,000 day-old chicks at PT. Anugerah Teknologi Ternak in Central Java, Indonesia. The NB-IoT system collected real-time data on environmental parameters and poultry activity. Time-series analysis revealed non-stationary data, while correlation analysis showed a strong negative relationship between temperature and humidity (r = -0.8521). Anomaly detection identified 13.33% of observations as anomalous, demonstrating the system's capability for early issue detection. Regression modeling (R-squared = 0.7261) indicated that temperature and humidity significantly influence poultry productivity. The study concludes that NB-IoT implementation in poultry farming has significant potential for enhancing productivity through real-time monitoring and early anomaly detection, supporting more efficient and sustainable precision farming practices. However, limitations in data stationarity and sample generalizability suggest the need for further research to improve long-term predictions and broaden applicability across diverse farming contexts.

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