IoT based Conceptual Framework for Monitoring Poultry Farms

Adha, Fatria Jumara; Johar, Gapar Md; Alkawaz, Mohammed Hazim; Hajamydeen, Asif Iqbal; Raya, Lilysuriazna

doi:10.1109/iscaie54458.2022.9794471

Cited by 8 publications

(2 citation statements)

References 10 publications

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“…Moreover, traditional inspection methods may miss the appropriate time for disease treatment, especially in cases of severe infectious diseases. With the modern technology development, internet of things (IoT) applications, video and image processing, classification capabilities, and smart poultry management have been emphasized in recent years [17]- [20]. The low cost of computing resources and common algorithms make contemporary technology an indispensable tool for monitoring vast farms and increasing production [21].…”

Section: Introductionmentioning

confidence: 99%

Poultry disease early detection methods using deep learning technology

Yajie,

Md Johar,

Iqbal Hajamydeen

2023

IJEECS

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<span>Poultry production is a pivotal contributor to global economic growth, playing a central role in promoting human ecosystem sustainability. It offers affordable and readily accessible protein sources, encompassing meat, eggs, and other by-products. Beyond its direct nutritional benefits, poultry production enhances household income, bolsters food security, and aids in poverty reduction, making it integral to worldwide economic advancement. However, as the global population surges, so does the demand for poultry meat and eggs. Concurrently, poultry disease management emerges as a paramount challenge, leading to significant threats to food security and economic stability. Leveraging cutting-edge technology offers promising avenues to devise strategies that not only bolster farm profitability but also mitigate environmental impacts and foster the well-being of both animals and humans. This study systematically reviews the latest literature concerning poultry disease diagnosis based on deep learning techniques, elucidating the clinical manifestations associated with various ailments. The analysis indicates that emerging technological solutions, especially image processing and deep learning (DL), substantially outperform conventional manual inspection methods in early disease detection and warning in the poultry sector. Such innovations underscore their potential for revolutionizing poultry health management and disease mitigation.</span>

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Section: Introductionmentioning

confidence: 99%

Poultry disease early detection methods using deep learning technology

Yajie,

Md Johar,

Iqbal Hajamydeen

2023

IJEECS

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“…One of the potential solutions is the integration of Internet of Things (IoT) into the healthcare sector [17]. The Internet of Things is considered as one of the feasible solutions for any remote value tracking problems such as health monitoring [4][5][6][7][8][9], agriculture [10,11], transportation [12] and green technology [13]. The advancement of this technology has given a significant impact on healthcare and well-being sector especially during the COVID-19 crisis.…”

Section: Introductionmentioning

confidence: 99%

Covid-19 Patient Health Monitoring System Using IoT

Abdullah

Raya

Norazman

et al. 2022

2022 IEEE 13th Control and System Graduate Research Colloquium (ICSGRC)

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Monitoring Covid-19 patients is extremely challenging due to under-resourced or risk of infection. With the increased demand for hospital beds and the difficulty of delivering care, some health centers have advised individual with milder symptoms to stay home. Hence, this paper presents a health monitoring system based on IoT that helps the medical staff to monitor blood saturation, heart rate, pulse rate and body temperature remotely. A Biosensor Module MAX3100 is used to read blood saturation level and heart rate of the patient while body temperature sensor, DS18B20 is employed to scan the body temperature. The measurement of room temperature and humidity level is done through humidity sensor. ESP32 Arduino will encode and decode all input data before execution process. The patient's fingers are connected to the sensors and the data is displayed on the smart phone or PC. The proposed system was tested and provide the intended output. Therefore, with the aid of this proposed system, medical staff can examine and keep track on several patients' status simultaneously and without the hassle of being infected by the virus as it is monitored remotely.

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