Real-Time Infection Detection System in Broiler Farm using MobileNetSSD Model
DOI:
https://doi.org/10.70882/josrar.2024.v1i1.21Keywords:
Poultry farm, Infection detection, MobileNetSSD, Real-time monitoring, Deep learningAbstract
The early detection of diseases in poultry farms is very important in safeguarding flock health and reducing economic losses. Outdated method of monitoring poultry health involves manual examinations, which are time consuming, labor-intensive and prone to inaccuracies. To curtail these challenges, this study presents a real-time infection detection system using lightweight object detection model called MobileNetSSD model for efficient and automated health monitoring. The system consists of deep learning techniques with affordable hardware that support real-time detection, tracking and analysis of broilers movement patterns in farm A, that consist of untagged healthy broilers and Red tagged sick broilers. The exercise was repeated three times to obtain movement threshold 84.9cm for sick broilers and 213.03cm healthy broilers, the outcome produced from reference farm A was used to analyze farm B and farm C. The model achieved 87% average accuracy, 93% average precision 78% average recall and 84.9% average F1 score. The integration of this system into existing farms, can lead to prompt interventions, curb the spread of infections and overall improvement in health management of broilers. This also research highlights the potential of computer vision in modifying poultry health monitoring practices, contributing to more sustainable and efficient poultry farming.
References
Bao, Yiqin et al. 2021. “Detection System of Dead and Sick Chickens in Large Scale Farms Based on Artificial Intelligence.” 18(May): 6117–35.
Chiu, Yu-chen et al. 2020. “Mobilenet-SSDv2 : An Improved Object Detection Model for Embedded Systems.” : 0–4.
Hammad, M, F Masoud, M Luqman, and M S Mahmood. 2022. “Molecular Techniques Are Better Choice as Compared to Conventional Methods for Pathotypic Characterization of Newcastle Disease Virus in Poultry Birds.” 10(May 2021): 50–54.
Jos, Juan, and Marcelino Mart. 2022. “Non-Antibiotics Strategies to Control Salmonella Infection in Poultry.” (Cdc): 1–29.
Kayabaşi, Ahmet. 2022. “Automatic Classification of Healthy and Sick Broilers in Terms of Avian Influenza by Using Neural Networks Sağlıklı ve Hasta Etlik Piliçlerin Kuş Gribi Açısından Sinir Ağları Kullanarak Otomatik Sınıflandırılması.” 4(2): 212–26.
Machuve, Dina, Ezinne Nwankwo, Neema Mduma, and Jimmy Mbelwa. 2018. “Poultry Diseases Diagnostics Models Using Deep Learning.” 2017.
Mathurabai, B, Vishnu Priya Maddali, Chaithya Devineni, and Iswarya Bhukya. 2022. “Object Detetcion Using SSD-MobileNet.” : 2668–71.
Mehta, Sachin. 2022. 1 Proceedings of a (preprint) CVNets : High Performance Library for Computer Vision. Association for Computing Machinery.
Meng, Jing, Ping Jiang, Jianmin Wang, and Kai Wang. 2022. “A MobileNet-SSD Model with FPN for Waste Detection A MobileNet ‑ SSD Model with FPN for Waste Detection.” Journal of Electrical Engineering & Technology (November 2021). https://doi.org/10.1007/s42835-021-00960-w.
Muwardi, Rachmat et al. 2023. “Human Object Detection for Real-Time Camera Using Mobilenet-SSD.” 3(2): 141–50.
Neethirajan, Suresh. 2022. “Automated Tracking Systems for the Assessment of Farmed Poultry.” Animal 12: 232.
Okinda, Cedric et al. 2019. “A Machine Vision System for Early Detection and Prediction of Sick Birds : A Broiler Chicken Model.”
Rajora, Shivam, Rahul Chhonkar, Shivangi Sharma, and Suhani Bhatia. 2022. “International Journal of Research Publication and Reviews Vehicle Feature Detection Using Image Processing and OPENCV.” 3(5): 482–85.
Robotics, Embedded. 2021. “Object Recognition and Localization Using Deep Convolutional Neural Network ( CNN ).”
Singh, Maninder. 2020. “Artificial Intelligence and IoT Based Monitoring of Poultry Health : A Review.” international conference on communication, network and satellite: 50–54.
Teddy, Firs et al. 2019. “Development of Smart Chicken Poultry Farm Using RTOS on Arduino.” (August): 27–29.
Younis, Ayesha, and Zhang Hai. 2022. “Real-Time Object Detection Using Pre-Trained Deep Learning Models Real-Time Object Detection Using Pre-Trained Deep Learning Models MobileNet- SSD.” (March 2020).
Zhang, Qingzhou, Zheng Yi Wu, Ming Zhao, and Jingyao Qi. 2017. “Automatic Partitioning of Water Distribution Networks Using Multiscale Community Detection and Multiobjective Optimization.” 143(2014).
Zhuang, Xiaolin et al. 2018. “Development of an Early Warning Algorithm to Detect Sick Broilers.” Computers and Electronics in Agriculture 144(July 2017): 102–13. https://doi.org/10.1016/j.compag.2017.11.032.
Zhuang, Xiaolin, and Tiemin Zhang. 2019. “ScienceDirect Detection of Sick Broilers by Digital Image Processing and Deep Learning.” Biosystems Engineering 179: 106–16. https://doi.org/10.1016/j.biosystemseng.2019.01.003.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Liman A. Doko, Eli Adama Jiya, Oyenike Mary Olanrewaju (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
