ArticlePloS one2026
IoT based automated early detection and real time environmental monitoring for poultry farming in developing countries.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Poultry farming is a rapidly growing agricultural subsector worldwide and plays a significant role in meeting global demand for human protein. In Bangladesh, poultry farming is in developing phase, but it faces major challenges. Where improper temperatures, humidity, gas concentrations, dust levels, and poor ventilation significantly reduce poultry health, egg production, and overall farm profitability. To address these issues, monitoring systems are increasingly being adopted for early detection and continuous assessment of the farm environment. However, in a traditional monitoring system, the process is slow and inaccurate, and it cannot provide early warning of harmful conditions. The conventional method for poultry farming involves manual monitoring, leading to poor management and low efficiency. Smart poultry farming using Internet of Things (IoT) technology can help improve efficiency, poultry well-being, and management practices. This paper presents an IoT based early detection, monitoring and controlling system to maintain an ideal environment in a poultry farm using real time data. The testing was conducted at a poultry farm in Bangladesh; for consistency, a rigorous 15-day test was conducted. The results from the prototype system shows significant results than those from the reference sensors. For each sensor used on the prototype system, a linear correlation analysis was conducted. This confirms the system's accuracy in measuring temperature, humidity, ammonia, and methane. To maintain the farm's temperature, an automated cooling system using a honeycomb is also incorporated. For the microcontroller unit, the ESP32 was used, and for the IoT base platform, ThingSpeak is utilized. After performing the cost analysis of the system, it was found that the system with the early monitoring feature was constructed at a total cost of United States Dollar (USD) 43, which is convenient for developing countries like Bangladesh. The source code of the proposed system is available on GitHub.
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