Design and Implementation of Smart Relay based Remote Monitoring and Controlling of Ammonia in Poultry Houses

Othman, Jawad K.; Mahmood, Jawad; Emarah, Ghazi Y. A. Al

doi:10.5120/18093-9149

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…Uric acid is present in the litter as the chickens consume heavy protein food. 18 In poultry, NH 3 concentration levels should be maintained between 10 and 25 ppm. A concentration level exceeding 35 ppm, sustained for 50 min, results in chick mortality.…”

Section: Smart Poultry Farmingmentioning

confidence: 99%

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A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

Goyal,

Yadav,

Mukherjee

2024

ACS Agric. Sci. Technol.

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The rapid growth of human population and frequent changes in environmental conditions pose challenges in providing nutrient-rich food to the current population. The demand for poultry products is increasing exponentially as they are a good source of low-cost proteins. Ensuring the well-being of birds and delivering nutritious poultry products is essential for both current and future food security needs and sustainable agriculture. This article is motivated by the potential of emerging digital technologies in agriculture to provide innovative solutions to challenges faced by farmers, particularly in poultry farming. It aims to shed light on various issues encountered by farmers like diseases on poultry farms and offers valuable insights for researchers seeking to address these challenges using the advancements in digital technologies. Precision farming in poultry involves leveraging technologies such as the internet of things, artificial intelligence, and edge computing to enhance animal health management. A smart poultry farm maintains the farm environment and detects diseases at early stage in the chickens. The farm resources are utilized optimally in the smart poultry farm. This research work provides a systematic literature review of intelligent systems designed for (i) poultry birds' health and welfare management using technologies like the internet of things (IoT), artificial intelligence, cloud computing, and edge computing and (ii) early disease detection, estimation of weight, and feeding behavior of birds with the use of computer vision and vocalization analysis. This article also explores various sensors employed in the development of IoT infrastructure. It has been demonstrated in the literature that modern digital technologies automate the management operations of poultry farms. As a result, the farmer's income increases, and high-quality food products are available to people at low cost.

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Section: Smart Poultry Farmingmentioning

confidence: 99%

“…The primary source of ammonia production is the microbial decomposition of nitrogenous substances such as uric acid in the litter. Uric acid is present in the litter as the chickens consume heavy protein food . In poultry, NH 3 concentration levels should be maintained between 10 and 25 ppm.…”

Section: Smart Poultry Farmingmentioning

confidence: 99%

A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

Goyal,

Yadav,

Mukherjee

2024

ACS Agric. Sci. Technol.

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“…The stools of poultry and the decomposition of organic matter generate numerous noxious gases and dust, which not only contaminate the environment but also raises the risk of spreading diseases [32][33]. The harmful gases like ammonia (NH3) are generated as a result of animal metabolism [34] and decaying animal waste. It is readily absorbed through the mucous membranes, harming the eyes and respiratory system of poultry.…”

Section: Noxious Gases and Dustmentioning

confidence: 99%

Poultry-Edge-AI-IoT System for Real-Time Monitoring and Predicting by Using Artificial Intelligence

Jebari

Mechkouri

Rekiek

et al. 2023

Int. J. Interact. Mob. Technol.

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Poultry farms have played a significant role throughout human history, in feeding the growing population. A good environment is a perfect condition for the growth of poultry, preventing disease, and effective production. The temperature higher and the humidity favor the growth of bacteria and hence the production of ammonia (NH3) by the decomposition of organic matter. Ammonia (NH3), carbon monoxide (CO), and carbon dioxide (CO2), Methane (CH4), hydrogen sulfide (H2S) are poisonous gases that can cause poultry diseases and mortality. The combination of Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing offer efficient and intelligent stand-alone systems of monitoring in real-time, predicting, and advanced automation. The paper aims to monitor in real-time and predict poultry barns' environmental conditions using an artificial intelligence algorithm. An intelligent system called Poultry-Edge-AI-IoT has been developed to gather, hash, store, pretreat, filter, knowledge extract, and transmit information from a heterogeneous wireless sensor network. The Poultry-Edge-AI-IoT system is based on IoT, AI, and Edge Computing for the detection of potential stress, the harmful gas concentration, and the prediction of poultry barns' environmental conditions. The system is modular and upgradeable.

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“…Unit Limit Influences Ammonia ppm 10 Ammonia is a toxic gas impairing animal performance and bird and staff health [41][42].…”

Section: Environmental Factormentioning

confidence: 99%

Pollution index of waterfowl farm assessment and prediction based on temporal convoluted network

et al. 2021

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Environmental quality is a major factor that directly impacts waterfowl productivity. Accurate prediction of pollution index (PI) is the key to improving environmental management and pollution control. This study applied a new neural network model called temporal convolutional network and a denoising algorithm called wavelet transform (WT) for predicting future 12-, 24-, and 48-hour PI values at a waterfowl farm in Shanwei, China. The temporal convoluted network (TCN) model performance was compared with that of recurrent architectures with the same capacity, long-short time memory neural network (LSTM), and gated recurrent unit (GRU). Denoised environmental data, including ammonia, temperature, relative humidity, carbon dioxide (CO2), and total suspended particles (TSP), were used to construct the forecasting model. The simulation results showed that the TCN model in general produced a more precise PI prediction and provided the highest prediction accuracy for all phases (MAE = 0.0842, 0.0859, and 0.1115; RMSE = 0.0154, 0.0167, and 0.0273; R2 = 0.9789, 0.9791, and 0.9635). The PI assessment prediction model based on TCN exhibited the best prediction accuracy and general performance compared with other parallel forecasting models and is a suitable and useful tool for predicting PI in waterfowl farms.

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Design and Implementation of Smart Relay based Remote Monitoring and Controlling of Ammonia in Poultry Houses

Cited by 6 publications

References 8 publications

A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

Poultry-Edge-AI-IoT System for Real-Time Monitoring and Predicting by Using Artificial Intelligence

Pollution index of waterfowl farm assessment and prediction based on temporal convoluted network

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