International Journal of Frontiers in Engineering Technology, 2023, 5(4); doi: 10.25236/IJFET.2023.050403.
Jiale Chen1, Zihao Ma2, Chong Li1
1School of Communication Engineering, College of Tongda Nanjing University of Posts and Telecommunications, Yangzhou, China
2School of Computer Engineering, College of Tongda Nanjing University of Posts and Telecommunications, Yangzhou, China
In view of the outstanding labor costs and high health and safety risks in the traditional poultry seedling sorting process, an automatic equipment suitable for the sorting of poultry seedlings was designed through the research on the extraction of the characteristics of a variety of poultry species. According to the different types of poultry seedlings, there are many different sorting requirements for chicken seedlings, duck seedlings, goose seedlings, etc., thus providing a classification and recognition method that combines visual information technology and convolutional neural network (CNN) technology to form a hardware and software collaboration framework to meet the functional requirements for image acquisition, processing and real-time detection of different poultry seedlings. First of all, we use camera technology to extract the features on the shape of the bird seedlings into the system and wait for the next step of correlation analysis; Then, based on CNN technology, different species of the same bird vaccine are classified; Finally, sorting is realized through hardware collaborative processing. For each kind of poultry seedlings, multiple trainings shall be conducted, and the corresponding database shall be established to improve the sorting accuracy. The results showed that the recognition accuracy of different types of poultry seedlings was 100%, the recognition accuracy of different types of poultry seedlings of the same species was 97.7%, and the recognition speed was 0.049 seconds, which could meet the requirements of efficient sorting of 8000 poultry seedlings per hour.
Convolution neural network; Sorting equipment; Visual technology
Jiale Chen, Zihao Ma, Chong Li. Intelligent poultry seedling sorting system based on convolutional neural network. International Journal of Frontiers in Engineering Technology (2023), Vol. 5, Issue 4: 12-18. https://doi.org/10.25236/IJFET.2023.050403.
[1] China's animal husbandry. Development situation of broiler industry in 2021 and outlook for 2022. Issue 3, 2022
[2] Gao Z, Wang A, Liu Y, et al. Intelligent Fresh-tea-leaves Sorting System Research Based on Convolution Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017.
[3] Zhang Ke, Feng Xiaohan, Guo Yurong, Su Yukun, Zhao Kai, Zhao Zhenbing, Ma Zhanyu, Ding Qiaolin 2021. Overview of depth convolution neural network models for image classification. Chinese Journal of Image Graphics, 26 (10): 2305-2325) [DOI: 10.11834/jig. 200302]
[4] Jia Heming, Lang Chunbo, Jiang Zichao. Plant leaf disease identification method based on lightweight convolution neural network DOI: 10. 11772/j. issn. 1001-9081.2020091471. 1001-9081 (2021) 06-1812-08.
[5] Che C, Wang H, Ni X, et al. Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network[J]. Industrial Lubrication and Tribology, 2020, ahead-of-print(ahead-of-print).
[6] Wang H, Cao Z, Hong B. A network intrusion detection system based on convolutional neural network [J]. Journal of intelligent & fuzzy systems: Applications in Engineering and Technology, 2020(6):38.
[7] Zhuang Yifan, et al. Innovative method for traffic data imputation based on convolutional neural network[J]. IET Intelligent Transport Systems, 2019.
[8] Duan Meng, Wang Gongpeng, Niu Changyong. Small sample image recognition method based on convolutional neural network. 1000-7024 (2018) 01-0224-06.10.16208/j.issn1000-7024.2018.01.039
[9] Zhai Guanghua. Five techniques for identifying male and female chicks. 1673-1085 (2010) 03-0025-01
[10] Dahl GE, Sainath TN, Hinton GE. Improving deep neural net-works for LVCSR using rectified linear units and dropout. [C]//IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver, BC, Canada: IEEE, 2013:8609-8613.
[11] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Delving deep into rectifiers: Surpassing human-level performance on Ima- geNet classification [C]//IEEE International Conference on Computer Vision. Chile:IEEE.2015:1026-1034.
[12] Wan L, Zeiler M, Zhang S, et al. Regularization of neural net- works using dropconnect [C]//Proceedings of the International Conference on Machine Learning. New York: ACM Press, 2013: 1058-1066.
[13] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, et al. Dropout: A simple way to prevent neural networks from overfitting [J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.