An enhancement algorithm for head characteristics of caged chickens detection based on cyclic consistent migration neural network.

Journal: Poultry science
PMID: 38603930

Abstract

The enclosed multistory poultry housing is a type of poultry enclosure widely used in industrial caged chicken breeding. Accurate identification and detection of the comb and eyes of caged chickens in poultry farms using this type of enclosure can enhance managers' understanding of the health of caged chickens. However, the accuracy of image detection of caged chickens will be affected by the enclosure's entrance, which will reduce the precision. Therefore, this paper proposes a cage-gate removal algorithm based on big data and deep learning Cyclic Consistent Migration Neural Network (CCMNN). The method achieves automatic elimination and restoration of some key information in the image through the CCMNN network. The Structural Similarity Index Measure (SSIM) between the recovered and original images on the test set is 91.14%. Peak signal-to-noise ratio (PSNR) is 25.34dB. To verify the practicability of the proposed method, the performance of the target detection algorithm is analyzed both before and after applying the CCMNN network in detecting the combs and eyes of caged chickens. Different YOLOv8 detection algorithms, including YOLOv8s, YOLOv8n, YOLOv8m, and YOLOv8x, were used to verify the algorithm proposed in this paper. The experimental results demonstrate that compared to images without CCMNN processing, the precision of comb detection of caged chickens is improved by 11, 11.3, 12.8, and 10.2%. Similarly, the precision of eye detection for caged chickens is improved by 2.4, 10.2, 6.8, and 9%. Therefore, more complete outline images of caged chickens can be obtained using this algorithm and the precision in detecting the comb and eyes of caged chickens can be enhanced. These advancements in the algorithm offer valuable insights for future poultry researchers aiming to deploy enhanced detection equipment, thereby contributing to the accurate assessment of poultry production and farm conditions.

Authors

  • Zhenwei Yu
    College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an 271018, China; Shandong Provincial Engineering Laboratory of Agricultural Equipment Intelligence, Shandong Provincial Key Laboratory of Horticultural Machineries and Equipment, Tai'an 271018, China.
  • Liqing Wan
    College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an 271018, China; Shandong Provincial Engineering Laboratory of Agricultural Equipment Intelligence, Shandong Provincial Key Laboratory of Horticultural Machineries and Equipment, Tai'an 271018, China.
  • Khurram Yousaf
    College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China.
  • Hai Lin
    Department of Endocrinology, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
  • Ji Zhang
    Department of Neurology, Xiangya Hospital, Central South University, Jiangxi, Nanchang, 330006, Jiangxi, China.
  • Hongchao Jiao
    College of Animal Science and Technology, Shandong Agriculture University, Tai'an 271018, China.
  • Geqi Yan
    College of Animal Science and Technology, Shandong Agriculture University, Tai'an 271018, China.
  • Zhanhua Song
    College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an 271018, China.
  • Fuyang Tian
    College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an 271018, China; Shandong Provincial Engineering Laboratory of Agricultural Equipment Intelligence, Shandong Provincial Key Laboratory of Horticultural Machineries and Equipment, Tai'an 271018, China. Electronic address: fytian@sdau.edu.cn.

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