ICCK Journal of Image Analysis and Processing
ISSN: 3068-6679 (Online)
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TY - JOUR AU - Alaeddine, Hmidi PY - 2026 DA - 2026/01/21 TI - Embedded Electronic IoT System for Poultry Health Monitoring and AI-Powered Disease Detection from Feces JO - ICCK Journal of Image Analysis and Processing T2 - ICCK Journal of Image Analysis and Processing JF - ICCK Journal of Image Analysis and Processing VL - 2 IS - 1 SP - 1 EP - 16 DO - 10.62762/JIAP.2025.569459 UR - https://www.icck.org/article/abs/JIAP.2025.569459 KW - ESP32 KW - YOLOV7 KW - MobileNetV3 KW - IoT KW - intelligent poultry system KW - disease detection AB - Poultry farming plays a vital role in global food production, requiring efficient management to ensure productivity and animal welfare. Traditional methods, largely based on manual monitoring, are often inefficient, error-prone, and costly. With the rise of Internet of Things (IoT) technologies, intelligent systems now enable remote monitoring and management of environmental conditions, farm operations, and disease prevention. Platforms such as ThingSpeak allow for real-time data collection, processing, and visualization, offering a cost-effective solution for poultry farm management. By integrating sensors to measure temperature, humidity, air quality, and feeding, and by leveraging ThingSpeak’s analytical tools, farms can automatically adjust conditions and support proactive decision-making. This not only reduces operational costs but also improves efficiency, resource management, and animal health monitoring. The growing demand for poultry products has pressured farms to increase production, which heightens the risk of disease outbreaks and significant economic losses. Traditional disease detection methods, which depend on manual inspections by skilled professionals, are labor-intensive and delay timely intervention. To address these challenges, an IoT-based poultry disease detection and classification system is proposed. This system employs sensors for continuous health monitoring and artificial intelligence algorithms such as YOLOV7 and MobileNetV3 to analyze data. YOLOV7 segments regions of interest from automatically captured fecal images, while MobileNetV3 classifies them into four states: healthy, coccidiosis, salmonella, and Newcastle disease. Trained on Zenodo database samples, these models achieve high accuracy, providing farmers and veterinarians with an effective tool for proactive disease management and sustainable poultry farming. SN - 3068-6679 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Alaeddine2026Embedded,
author = {Hmidi Alaeddine},
title = {Embedded Electronic IoT System for Poultry Health Monitoring and AI-Powered Disease Detection from Feces},
journal = {ICCK Journal of Image Analysis and Processing},
year = {2026},
volume = {2},
number = {1},
pages = {1-16},
doi = {10.62762/JIAP.2025.569459},
url = {https://www.icck.org/article/abs/JIAP.2025.569459},
abstract = {Poultry farming plays a vital role in global food production, requiring efficient management to ensure productivity and animal welfare. Traditional methods, largely based on manual monitoring, are often inefficient, error-prone, and costly. With the rise of Internet of Things (IoT) technologies, intelligent systems now enable remote monitoring and management of environmental conditions, farm operations, and disease prevention. Platforms such as ThingSpeak allow for real-time data collection, processing, and visualization, offering a cost-effective solution for poultry farm management. By integrating sensors to measure temperature, humidity, air quality, and feeding, and by leveraging ThingSpeak’s analytical tools, farms can automatically adjust conditions and support proactive decision-making. This not only reduces operational costs but also improves efficiency, resource management, and animal health monitoring. The growing demand for poultry products has pressured farms to increase production, which heightens the risk of disease outbreaks and significant economic losses. Traditional disease detection methods, which depend on manual inspections by skilled professionals, are labor-intensive and delay timely intervention. To address these challenges, an IoT-based poultry disease detection and classification system is proposed. This system employs sensors for continuous health monitoring and artificial intelligence algorithms such as YOLOV7 and MobileNetV3 to analyze data. YOLOV7 segments regions of interest from automatically captured fecal images, while MobileNetV3 classifies them into four states: healthy, coccidiosis, salmonella, and Newcastle disease. Trained on Zenodo database samples, these models achieve high accuracy, providing farmers and veterinarians with an effective tool for proactive disease management and sustainable poultry farming.},
keywords = {ESP32, YOLOV7, MobileNetV3, IoT, intelligent poultry system, disease detection},
issn = {3068-6679},
publisher = {Institute of Central Computation and Knowledge}
}
Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 
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