Khang Hoang Nguyen
ABSTRACT
Among the causes of reduced production is a chicken disease, which can negatively affect consumer health. With the advancement of computer vision technology and profound innovations in the field of research, it has become increasingly important to analyze disease images collected by sensors in chickens to analyze the possibility of infection conveniently and efficiently. Consequently, research proposes to identify lesions using the Autoencoder and Yolov6 model to classify and detect diseases in chicken flocks. This model is suitable for different chicken breeds from many countries and regions. This method helps improve and enhance image recognition accuracy by incorporating the data enhancement method in the data preprocessing step. The results show that the value of val/mAP (average accuracy) obtained by the method proposed in this paper is 99.15%. Moreover, hit over 90% on the test dataset. This method can be applied to the early detection of disease-carrying chickens in the captive population, ensuring a quality food source for humans.
- Ghufran Ahmed, Rauf Ahmed Shams Malick, Adnan Akhunzada, Sumaiyah Zahid, Muhammad Rabeet Sagri, and Abdullah Gani. 2021. An Approach towards IoT-Based Predictive Service for Early Detection of Diseases in Poultry Chickens. Sustainability 13, 23 (December 2021), 13396. DOI:https://doi.org/10.3390/su132313396Google Scholar
Cross Ref
- Mohammed A. Al-masni, Mugahed A. Al-antari, Jeong-Min Park, Geon Gi, Tae-Yeon Kim, Patricio Rivera, Edwin Valarezo, Mun-Taek Choi, Seung-Moo Han, and Tae-Seong Kim. 2018. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput. Methods Programs Biomed. 157, (April 2018), 85–94. DOI:https://doi.org/10.1016/j.cmpb.2018.01.017Google ScholarDigital Library
- Dor Bank, Noam Koenigstein, and Raja Giryes. 2020. Autoencoders. (2020). DOI:https://doi.org/10.48550/ARXIV.2003.05991Google ScholarCross Ref
- Gary Conley, Stephanie Castle Zinn, Taylor Hanson, Krista McDonald, Nicole Beck, and Howard Wen. 2022. Using a deep learning model to quantify trash accumulation for cleaner urban stormwater. Comput. Environ. Urban Syst. 93, (April 2022), 101752. DOI:https://doi.org/10.1016/j.compenvurbsys.2021.101752Google ScholarCross Ref
- Christine Dewi, Rung-Ching Chen, Yan-Ting Liu, Xiaoyi Jiang, and Kristoko Dwi Hartomo. 2021. Yolo V4 for Advanced Traffic Sign Recognition With Synthetic Training Data Generated by Various GAN. IEEE Access 9, (2021), 97228–97242. DOI:https://doi.org/10.1109/ACCESS.2021.3094201Google ScholarCross Ref
- Xuelong Hu, Yang Liu, Zhengxi Zhao, Jintao Liu, Xinting Yang, Chuanheng Sun, Shuhan Chen, Bin Li, and Chao Zhou. 2021. Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network. Comput. Electron. Agric. 185, (June 2021), 106135. DOI:https://doi.org/10.1016/j.compag.2021.106135Google ScholarCross Ref
- Junduan Huang, Wenqing Wang, and Tiemin Zhang. 2019. Method for detecting avian influenza disease of chickens based on sound analysis. Biosyst. Eng. 180, (April 2019), 16–24. DOI:https://doi.org/10.1016/j.biosystemseng.2019.01.015Google ScholarCross Ref
- Margrit Kasper-Eulaers, Nico Hahn, Stian Berger, Tom Sebulonsen, Øystein Myrland, and Per Egil Kummervold. 2021. Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5. Algorithms 14, 4 (March 2021), 114. DOI:https://doi.org/10.3390/a14040114Google ScholarCross Ref
- Chuyi Li, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng, Liang Li, Zaidan Ke, Qingyuan Li, Meng Cheng, Weiqiang Nie, Yiduo Li, Bo Zhang, Yufei Liang, Linyuan Zhou, Xiaoming Xu, Xiangxiang Chu, Xiaoming Wei, and Xiaolin Wei. 2022. YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. (2022). DOI:https://doi.org/10.48550/ARXIV.2209.02976Google ScholarCross Ref
- David A.C. Manning. 2015. How will minerals feed the world in 2050? Proc. Geol. Assoc. 126, 1 (February 2015), 14–17. DOI:https://doi.org/10.1016/j.pgeola.2014.12.005Google ScholarCross Ref
- Hope Mbelwa. 2021. Deep Convolutional Neural Network for Chicken Diseases Detection. Int. J. Adv. Comput. Sci. Appl. 12, 2 (2021), 8.Google Scholar
- Cedric Okinda, Mingzhou Lu, Longshen Liu, Innocent Nyalala, Caroline Muneri, Jintao Wang, Hailin Zhang, and Mingxia Shen. 2019. A machine vision system for early detection and prediction of sick birds: A broiler chicken model. Biosyst. Eng. 188, (December 2019), 229–242. DOI:https://doi.org/10.1016/j.biosystemseng.2019.09.015Google ScholarCross Ref
- Keiron O'Shea and Ryan Nash. 2015. An Introduction to Convolutional Neural Networks. (2015). DOI:https://doi.org/10.48550/ARXIV.1511.08458Google ScholarCross Ref
- Luyl-Da Quach, Nghi Pham-Quoc, Duc Chung Tran, and Mohd. Fadzil Hassan. 2020. Identification of Chicken Diseases Using VGGNet and ResNet Models. In Industrial Networks and Intelligent Systems, Nguyen-Son Vo and Van-Phuc Hoang (eds.). Springer International Publishing, Cham, 259–269. DOI:https://doi.org/10.1007/978-3-030-63083-6_20Google ScholarCross Ref
- Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You Only Look Once: Unified, Real-Time Object Detection. Retrieved October 24, 2022 from http://arxiv.org/abs/1506.02640Google Scholar
- My Vo Dang Uyen, Nha Truong Thanh, Anh Nguyen My, Hang Lam Thi Khanh, Lan Le Thi Thu, and Luyl-Da Quach. 2021. MobileNetV2 in the Classification of Avian Influenza and CRD in Chickens. In Software Engineering Application in Informatics, Radek Silhavy, Petr Silhavy and Zdenka Prokopova (eds.). Springer International Publishing, Cham, 668–678. DOI:https://doi.org/10.1007/978-3-030-90318-3_53Google ScholarCross Ref
- Dihua Wu, Shuaichao Lv, Mei Jiang, and Huaibo Song. 2020. Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Comput. Electron. Agric. 178, (November 2020), 105742. DOI:https://doi.org/10.1016/j.compag.2020.105742Google ScholarCross Ref
- Wentong Wu, Han Liu, Lingling Li, Yilin Long, Xiaodong Wang, Zhuohua Wang, Jinglun Li, and Yi Chang. 2021. Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image. PLOS ONE 16, 10 (October 2021), e0259283. DOI:https://doi.org/10.1371/journal.pone.0259283Google ScholarCross Ref
- Zhenwei Yu, Yonggang Shen, and Chenkai Shen. 2021. A real-time detection approach for bridge cracks based on YOLOv4-FPM. Autom. Constr. 122, (February 2021), 103514. DOI:https://doi.org/10.1016/j.autcon.2020.103514Google ScholarCross Ref
- Jianming Zhang, Manting Huang, Xiaokang Jin, and Xudong Li. 2017. A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2. Algorithms 10, 4 (November 2017), 127. DOI:https://doi.org/10.3390/a10040127Google ScholarCross Ref
- Liquan Zhao and Shuaiyang Li. 2020. Object Detection Algorithm Based on Improved YOLOv3. Electronics 9, 3 (March 2020), 537. DOI:https://doi.org/10.3390/electronics9030537Google ScholarCross Ref
Index Terms
- Combining Autoencoder and Yolov6 Model for Classification and Disease Detection in Chickens
Recommendations
A Deep Convolutional Autoencoder-Based Approach for Parkinson’s Disease Diagnosis Through Speech Signals
Advanced Data Mining and ApplicationsAbstractParkinson’s Disease (PD) is a neurodegenerative disease that primarily manifests through cognitive, motor and speech disorders. But it has been proven that voice changes in Parkinson’s patients are among the symptoms that appear early. In this ...
Determining the severity of Parkinson’s disease in patients using a multi task neural network
AbstractParkinson’s disease is easy to diagnose when it is advanced, but it is very difficult to diagnose in its early stages. Early diagnosis is essential to be able to treat the symptoms. It impacts on daily activities and reduces the quality of life of ...
Real-Time Neurodegenerative Disease Video Classification with Severity Prediction
Image Analysis and Processing – ICIAP 2019AbstractIn this paper, an automatic diagnosis system for neurodegenerative diseases is presented. Starting with an existing neurodegenerative diseases gait dataset, namely the NDDGD dataset, classification and regression algorithms have been trained, with ...
Comments