Design and Implementation of Monitoring and Control System for a Poultry Farm

Edwan, Ezzaldeen; Qassem, Mohammed Abu; Al-Roos, Said Abu; Elnaggar, Mohamed; Ahmed, Gassan; Ahmed, Aya Sayed; Zaqout, Ahmed

doi:10.1109/icpet51420.2020.00017

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Infectious diseases must be diagnosed early to control disease spread across a flock and provide treatment to decrease substantial mortality rates. On the other hand, non-infectious factors such as overcrowding [6], neoplasia [1], extreme temperatures [7], poor nutrition [1], and poor ventilation [7] might also contribute to mortality inside poultry housing. For example, heat stress can cause chickens to die in large numbers, especially during hot weather conditions [8].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Cage-Free Dead Hens with Deep Learning Methods

et al. 2023

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Poultry farming plays a significant role in ensuring food security and economic growth in many countries. However, various factors such as feeding management practices, environmental conditions, and diseases lead to poultry mortality (dead birds). Therefore, regular monitoring of flocks and timely veterinary assistance is crucial for maintaining poultry health, well-being, and the success of poultry farming operations. However, the current monitoring method relies on manual inspection by farm workers, which is time-consuming. Therefore, developing an automatic early mortality detection (MD) model with higher accuracy is necessary to prevent the spread of infectious diseases in poultry. This study aimed to develop, evaluate, and test the performance of YOLOv5-MD and YOLOv6-MD models in detecting poultry mortality under various cage-free (CF) housing settings, including camera height, litter condition, and feather coverage. The results demonstrated that the YOLOv5s-MD model performed exceptionally well, achieving a high mAP@0.50 score of 99.5%, a high FPS of 55.6, low GPU usage of 1.04 GB, and a fast-processing time of 0.4 h. Furthermore, this study also evaluated the models’ performances under different CF housing settings, including different levels of feather coverage, litter coverage, and camera height. The YOLOv5s-MD model with 0% feathered covering achieved the best overall performance in object detection, with the highest mAP@0.50 score of 99.4% and a high precision rate of 98.4%. However, 80% litter covering resulted in higher MD. Additionally, the model achieved 100% precision and recall in detecting hens’ mortality at the camera height of 0.5 m but faced challenges at greater heights such as 2 m. These findings suggest that YOLOv5s-MD can detect poultry mortality more accurately than other models, and its performance can be optimized by adjusting various CF housing settings. Therefore, the developed model can assist farmers in promptly responding to mortality events by isolating affected birds, implementing disease prevention measures, and seeking veterinary assistance, thereby helping to reduce the impact of poultry mortality on the industry, ensuring the well-being of poultry and the overall success of poultry farming operations.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection of Cage-Free Dead Hens with Deep Learning Methods

et al. 2023

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“…Chicks are also very sensitive to temperatures, especially when their age is less than six weeks, and an ambient temperature must be maintained to recommended values [6]. A legacy poultry farm requires extensive staff and labor for both taking care of the birds and keeping an eye on their medical condition [7,8]. An autonomous farming system for ground birds with minimal human intervention helps in ubiquitous monitoring with reduced operational costs [9,10].…”

Section: Introductionmentioning

confidence: 99%