Version 1
: Received: 30 August 2024 / Approved: 1 September 2024 / Online: 2 September 2024 (11:37:57 CEST)
How to cite:
Singh, H.; Kajla, A. K. Optimizing Ammonia Concentration in Poultry Houses through Random Forest Prediction and LoRa-WAN Monitoring & Controlling System. Preprints2024, 2024090042. https://doi.org/10.20944/preprints202409.0042.v1
Singh, H.; Kajla, A. K. Optimizing Ammonia Concentration in Poultry Houses through Random Forest Prediction and LoRa-WAN Monitoring & Controlling System. Preprints 2024, 2024090042. https://doi.org/10.20944/preprints202409.0042.v1
Singh, H.; Kajla, A. K. Optimizing Ammonia Concentration in Poultry Houses through Random Forest Prediction and LoRa-WAN Monitoring & Controlling System. Preprints2024, 2024090042. https://doi.org/10.20944/preprints202409.0042.v1
APA Style
Singh, H., & Kajla, A. K. (2024). Optimizing Ammonia Concentration in Poultry Houses through Random Forest Prediction and LoRa-WAN Monitoring & Controlling System. Preprints. https://doi.org/10.20944/preprints202409.0042.v1
Chicago/Turabian Style
Singh, H. and Ashok Kumar Kajla. 2024 "Optimizing Ammonia Concentration in Poultry Houses through Random Forest Prediction and LoRa-WAN Monitoring & Controlling System" Preprints. https://doi.org/10.20944/preprints202409.0042.v1
Abstract
Balancing the levels of ammonia, in poultry facilities plays a role in enhancing the welfare and productivity of animals. Various environmental factors such as temperature, humidity, ammonia concentrations, carbon dioxide concentration and exposure to light have an impact on the wellbeing and efficiency of poultry. It is essential to monitor these elements to achieve outcomes necessitating practical, reliable and cost-effective solutions. Leveraging the Internet of Things (IoT) in conjunction with LoRa-WAN (Long Range Wide Area Network) technology presents an opportunity in the industry by enhancing connectivity among devices and facilitating real time data transmission. This research introduces a hardware and software setup for monitoring conditions at poultry farms through LoRa WAN and IoT technologies. More-over researchers employed Random Forest Machine Learning prediction models to tune the concentration of Ammonia (NH3) in poultry facilities across a wide area or multiple Poultry houses. Comparative assessments were conducted to evaluate the proposed system against devices revealing a correlation exceeding 0.9830 between the sensors and standard equipment while costing 28% less than traditional tools. The prototype displayed outcomes underscoring its potential, for managing poultry facilities across multiple farms with centralized oversight
Keywords
Machine Learning; Random Forest; LoRa (Long range); Internet of Things; Smart Poultry Farm; Ammonia gas Optimization
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
