Classification of Unhealthy Chicken based on Chromaticity of the Comb

Bakar, Mohd Anif Akhmal Abu; Ker, Pin Jern; Tang, Shirley G. H.; Lee, Hui Jing; Zainal, Biddatul Syirat

doi:10.1109/icoco56118.2022.10031812

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…A logistic function is used to represent the dependent variable in the machine learning classification technique known as logistic regression [14] [15]. The logistic function is commonly referred to as the sigmoid function and is a mathematical operation that converts an input real number into a probability value between 0 and 1.…”

Section: B Logistic Regressionmentioning

confidence: 99%

AIoT-Driven Edge Computing for Rural Small-Scale Poultry Farming: Smart Environmental Monitoring and Anomaly Detection for Enhanced Productivity

Gandhi K

2023

IJRITCC

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The growing demand for chicken production has emphasized the importance of maintaining optimal conditions to improve quality and productivity.The integration of Artificial Intelligence (AI) and the Internet of Things(IoT) is recommended for the efficient management of the farm's environment. A potential solution is presented in this paper, utilizing IoT-based sensor nodes with ARM Cortex M3 - LPC 1769 and LORA technology to monitor chicken farms across diverse regions.The proposed solution incorporates a low-cost edge computing server-Jetson Nano device equipped with a machine learning model to categorize and monitor live environmental conditions in poultry farms. Real-time data from various branches is collected and analyzed using machine learning classification techniques including logistic regression, K nearest neighbors, and support vector machines.The performance of these algorithms is compared to identify the most effective approach. Upon evaluation, the K nearest neighbors emerges as the superior performer, achieving an impressive accuracy of 99.72% and an execution duration of 0.087 seconds on the Jetson Nano edge computing device. This cost-effective technology is tailored for small businesses in regions where farmers can gain valuable insights from data-driven decisions and closely monitor their operations. By incorporating AIoT into farm management, the challenges faced by small-scale poultry farming can be addressed, empowering farmers with enlightened techniques to improve overall productivity and quality.

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Section: B Logistic Regressionmentioning

confidence: 99%

AIoT-Driven Edge Computing for Rural Small-Scale Poultry Farming: Smart Environmental Monitoring and Anomaly Detection for Enhanced Productivity

Gandhi K

2023

IJRITCC

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“…This information is then used to improve their health condition, and ultimately, enhance production efficiency and profitability. In addition, an effective system for detecting and analyzing chicken behavior contributes in early disease detection, preventing outbreaks [1], ensuring food safety, protecting public health [2], and ensuring the quality of poultry production [3]. Hence, having a reliable system for monitoring chicken behavior is essential for sustainable poultry farming and ensuring the well-being of the birds.…”

Section: Introductionmentioning

confidence: 99%

Chicken Behavior Analysis for Surveillance in Poultry Farms

Mohialdin¹,

Elbarrany²,

Atia³

2023

IJACSA

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Poultry farming is an important industry that provides food for a growing population. However, the welfare of the birds is a major concern, as poor living conditions leads to abnormal behavior that affects the health and productivity of the flock. In order to monitor and improve the welfare of the birds, it is important to have a surveillance system in place that monitors the behavior of the chickens and alert farmers to potential issues. This paper reviews the current state of the art in behavior analysis for surveillance in poultry farms and discuss potential future directions for research in this area. This paper presents a computer-vision-based system that detects and monitors the behaviors of the chickens in poultry farms. The system classifies three behaviors which are eating, walking and sleeping. The system takes videos as input and then classifies the behavior of the chicken. The proposed system produces an accuracy of 94.7% using Light Gradient Boosting Machine on a collected data-set of chickens, and a 98.4% accuracy on a benchmarked Human Activity Recognition data-set.

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“…The proposed model achieved an average accuracy of 73.5%, and showed positive influences on turkey management, allowing for clear differentiation between individual animals even in crowded situations [56]. Bakar et al [57] developed a supervised machine learning algorithm for early detection of bacteria-or virus-infected chickens using the International Commission on Illumination (CIE) XYZ color space. The algorithm uses a logistic regression model to classify chickens, revealing 100% sensitivity and 83% specificity.…”

mentioning

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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Classification of Unhealthy Chicken based on Chromaticity of the Comb

Cited by 7 publications

References 16 publications

AIoT-Driven Edge Computing for Rural Small-Scale Poultry Farming: Smart Environmental Monitoring and Anomaly Detection for Enhanced Productivity

AIoT-Driven Edge Computing for Rural Small-Scale Poultry Farming: Smart Environmental Monitoring and Anomaly Detection for Enhanced Productivity

Chicken Behavior Analysis for Surveillance in Poultry Farms

Poultry disease early detection methods using deep learning technology

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