Exploratory Study of Sex Identification for Chicken Embryos Based on Blood Vessel Images and Deep Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection
2.1.1. Materials and Instruments
2.1.2. Image Acquisition System
2.1.3. Data Acquisition Experiment
2.2. Dataset Construction
2.3. YOLOv7 Deep Learning Network
2.4. Improvement of the YOLOv7 Algorithm
2.4.1. Backbone Attention Module
2.4.2. Multi-Scale Features Fusion
2.4.3. Loss Algorithm
2.5. Experimental Setup and Evaluation Metrics
3. Results and Discussion
3.1. Relationship between External Morphological Characteristics and Sex of Chicken Eggs
3.2. Algorithm Performance Evaluation
3.2.1. Results of Different Incubation Days
3.2.2. Impact of YOLOv7 Improvements on Sex Identification of Chicken Eggs
3.3. Comparison of Different Object Detection Algorithms
3.4. Comparison with Existing Research
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Incubation Day | Class | No. of Eggs | No. of Images | |||
---|---|---|---|---|---|---|
Complete | Slightly Rotated | Partially Obscured | Total | |||
Day 3 | Female | 1418 | 2246 | 457 | 34 | 2737 |
Male | 1426 | 2259 | 473 | 30 | 2762 | |
Day 4 | Female | 1418 | 2096 | 685 | 185 | 2966 |
Male | 1426 | 2105 | 690 | 199 | 2994 | |
Day 5 | Female | 1418 | 1628 | 692 | 312 | 2632 |
Male | 1426 | 1634 | 707 | 329 | 2670 | |
Total | 2844 | 11,968 | 3704 | 1089 | 16,761 |
Incubation Day | Class | Training Set | Testing Set | ||
---|---|---|---|---|---|
No. of Eggs | No. of Images | No. of Eggs | No. of Images | ||
Day 3 | Female | 1000 | 1928 | 418 | 809 |
Male | 1000 | 1944 | 426 | 818 | |
Day 4 | Female | 1000 | 2124 | 418 | 842 |
Male | 1000 | 2127 | 426 | 847 | |
Day 5 | Female | 1000 | 1870 | 418 | 762 |
Male | 1000 | 1884 | 426 | 786 |
Class | Length | Width | Shape Index | Weight | Eccentricity |
---|---|---|---|---|---|
Female | 55.2755 ± 2.3654 | 42.7008 ± 1.5772 | 1.2948 ± 0.0465 | 56.9026 ± 5.7508 | 0.6326 ± 0.0343 |
Male | 54.9282 ± 2.1884 | 42.6404 ± 1.5810 | 1.2890 ± 0.0494 | 56.4981 ± 5.1345 | 0.6279 ± 0.0362 |
Incubation Day | Class | Acc (%) | P (%) | R (%) | AP (%) | mAP (%) |
---|---|---|---|---|---|---|
Day 3 | Female | 87.68 | 88.60 | 89.05 | 86.05 | 85.81 |
Male | 86.43 | 87.17 | 88.23 | 85.57 | ||
Day 4 | Female | 90.32 | 93.21 | 83.39 | 89.67 | 88.79 |
Male | 88.18 | 95.36 | 82.01 | 87.91 | ||
Day 5 | Female | 85.23 | 83.60 | 81.39 | 84.73 | 82.29 |
Male | 84.12 | 78.27 | 80.64 | 79.85 |
Module | Exp No. 1 | Exp No. 2 | Exp No. 3 | Exp No. 4 |
---|---|---|---|---|
CBAM | × | √ | × | √ |
BiFPN | × | × | √ | √ |
mAP (%) | 84.82 | 87.11 | 86.65 | 88.28 |
Algorithm | P (%) | R (%) | AP (%) | mAP (%) | Processing Time (ms) | Model Size (MB) |
---|---|---|---|---|---|---|
Faster R-CNN | 45.62 | 89.73 | 73.88 | 71.62 | 52.63 | 118.60 |
SSD | 36.10 | 82.41 | 58.49 | 56.30 | 8.40 | 97.30 |
YOLOv5 | 92.23 | 82.58 | 84.66 | 82.83 | 31.25 | 15.90 |
Ours | 94.05 | 85.12 | 89.61 | 88.79 | 23.90 | 11.80 |
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Jia, N.; Li, B.; Zhao, Y.; Fan, S.; Zhu, J.; Wang, H.; Zhao, W. Exploratory Study of Sex Identification for Chicken Embryos Based on Blood Vessel Images and Deep Learning. Agriculture 2023, 13, 1480. https://doi.org/10.3390/agriculture13081480
Jia N, Li B, Zhao Y, Fan S, Zhu J, Wang H, Zhao W. Exploratory Study of Sex Identification for Chicken Embryos Based on Blood Vessel Images and Deep Learning. Agriculture. 2023; 13(8):1480. https://doi.org/10.3390/agriculture13081480
Chicago/Turabian StyleJia, Nan, Bin Li, Yuliang Zhao, Shijie Fan, Jun Zhu, Haifeng Wang, and Wenwen Zhao. 2023. "Exploratory Study of Sex Identification for Chicken Embryos Based on Blood Vessel Images and Deep Learning" Agriculture 13, no. 8: 1480. https://doi.org/10.3390/agriculture13081480
APA StyleJia, N., Li, B., Zhao, Y., Fan, S., Zhu, J., Wang, H., & Zhao, W. (2023). Exploratory Study of Sex Identification for Chicken Embryos Based on Blood Vessel Images and Deep Learning. Agriculture, 13(8), 1480. https://doi.org/10.3390/agriculture13081480