An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms
Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided di...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Korean Society of Animal Sciences and Technology
2023-03-01
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| Series: | Journal of Animal Science and Technology |
| Subjects: | |
| Online Access: | http://www.ejast.org/archive/view_article?doi=10.5187/jast.2022.e107 |
| _version_ | 1797847953416650752 |
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| author | Jung-woo Chae Yo-han Choi Jeong-nam Lee Hyun-ju Park Yong-dae Jeong Eun-seok Cho Young-sin Kim Tae-kyeong Kim Soo-jin Sa Hyun-chong Cho |
| author_facet | Jung-woo Chae Yo-han Choi Jeong-nam Lee Hyun-ju Park Yong-dae Jeong Eun-seok Cho Young-sin Kim Tae-kyeong Kim Soo-jin Sa Hyun-chong Cho |
| author_sort | Jung-woo Chae |
| collection | DOAJ |
| description | Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise. |
| first_indexed | 2024-04-09T18:19:36Z |
| format | Article |
| id | doaj.art-1b50872e9b3b4587b7131f44250d17b9 |
| institution | Directory Open Access Journal |
| issn | 2672-0191 2055-0391 |
| language | English |
| last_indexed | 2024-04-09T18:19:36Z |
| publishDate | 2023-03-01 |
| publisher | Korean Society of Animal Sciences and Technology |
| record_format | Article |
| series | Journal of Animal Science and Technology |
| spelling | doaj.art-1b50872e9b3b4587b7131f44250d17b92023-04-12T08:25:44ZengKorean Society of Animal Sciences and TechnologyJournal of Animal Science and Technology2672-01912055-03912023-03-0165236537610.5187/jast.2022.e107An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithmsJung-woo Chae0Yo-han Choi1Jeong-nam Lee2Hyun-ju Park3Yong-dae Jeong4Eun-seok Cho5Young-sin Kim6Tae-kyeong Kim7Soo-jin Sa8Hyun-chong Cho9Interdisciplinary Graduate Program for BIT Medical Convergence, Kangwon National University, Chuncheon 24341, KoreaSwine Science Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, KoreaInterdisciplinary Graduate Program for BIT Medical Convergence, Kangwon National University, Chuncheon 24341, KoreaSwine Science Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, KoreaSwine Science Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, KoreaSwine Science Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, KoreaSwine Science Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, KoreaDepartment of Electronics Engineering, Kangwon National University, Chuncheon 24341, KoreaSwine Science Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, KoreaDepartment of Electronics Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence, Kangwon National University, Chuncheon 24341, KoreaPig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise. http://www.ejast.org/archive/view_article?doi=10.5187/jast.2022.e107Classification algorithmDeep learningPregnancy diagnosisSowUltrasound |
| spellingShingle | Jung-woo Chae Yo-han Choi Jeong-nam Lee Hyun-ju Park Yong-dae Jeong Eun-seok Cho Young-sin Kim Tae-kyeong Kim Soo-jin Sa Hyun-chong Cho An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms Journal of Animal Science and Technology Classification algorithm Deep learning Pregnancy diagnosis Sow Ultrasound |
| title | An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms |
| title_full | An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms |
| title_fullStr | An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms |
| title_full_unstemmed | An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms |
| title_short | An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms |
| title_sort | intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms |
| topic | Classification algorithm Deep learning Pregnancy diagnosis Sow Ultrasound |
| url | http://www.ejast.org/archive/view_article?doi=10.5187/jast.2022.e107 |
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