Using an Optoelectronic Method for the Non-Destructive Sorting of Hatching Duck Eggs
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| I publikationen: | AgriEngineering vol. 7, no. 12 (2025), p. 411-431 |
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| Huvudupphov: | |
| Övriga upphov: | , , , , |
| Utgiven: |
MDPI AG
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| Ämnen: | |
| Länkar: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3286238586 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2624-7402 | ||
| 024 | 7 | |a 10.3390/agriengineering7120411 |2 doi | |
| 035 | |a 3286238586 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Shokhan, Alpeisov |u Faculty of Veterinary Medicine and Animal Science, Kazakh National Agrarian Research University, Almaty 050010, Kazakhstan; shokhan.alpeisov@kaznaru.edu.kz (S.A.); otebayev.zhassulan@kaznaru.edu.kz (Z.O.) | |
| 245 | 1 | |a Using an Optoelectronic Method for the Non-Destructive Sorting of Hatching Duck Eggs | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The efficient pre-incubation selection of duck eggs is essential to ensuring stable hatchability, but most existing optoelectronic and machine vision systems have been calibrated for chicken eggs and cannot be directly used for duck eggs because of their larger size, stronger reflectivity and wider morphological variability. This study proposes an optoelectronic method specifically adapted to Adigel duck eggs that combines load cell weighing, infrared distance sensing and dual-projection image processing in a single stationary setup. A total of 300 eggs were measured manually and automatically, and the results were statistically compared. The developed algorithm uses adaptive Gaussian thresholding (51 × 51, C = 2) and a median 5 × 5 filter to stabilize contour extraction on glossy and spotted shells, followed by ellipsoid-based volume estimation with a breed-specific correction factor (Kv = 0.637). The automatic system showed high agreement with manual measurements (r > 0.95 for mass and linear dimensions) and a mean relative error below 2%. Density, the shape index (If) and the shape coefficient (K1) were computed to classify eggs into “suitable”, “borderline” and “unsuitable” categories. A preliminary incubation trial (n = 60) of eggs classified as “suitable” resulted in 92% hatchability, thus confirming the predictive value of the proposed criteria. Unlike chicken-oriented systems, the presented solution provides breed-specific calibration and can be implemented in small and medium hatcheries for the reproducible, non-destructive sorting of hatching duck eggs. | |
| 653 | |a Software | ||
| 653 | |a Ducks | ||
| 653 | |a Accuracy | ||
| 653 | |a Nondestructive testing | ||
| 653 | |a Aquatic birds | ||
| 653 | |a Vision systems | ||
| 653 | |a Optoelectronics | ||
| 653 | |a Waterfowl | ||
| 653 | |a Image processing | ||
| 653 | |a Eggs | ||
| 653 | |a Hatcheries | ||
| 653 | |a Automation | ||
| 653 | |a Bird eggs | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Hatching | ||
| 653 | |a Genetic variability | ||
| 653 | |a Hatchability | ||
| 653 | |a Adaptive algorithms | ||
| 653 | |a Strain gauges | ||
| 653 | |a Agriculture | ||
| 653 | |a Quality standards | ||
| 653 | |a Cameras | ||
| 653 | |a Machine vision | ||
| 653 | |a Computer vision | ||
| 653 | |a Farms | ||
| 653 | |a Chickens | ||
| 653 | |a Fertility | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Poultry | ||
| 653 | |a Morphology | ||
| 653 | |a Reproducibility | ||
| 653 | |a Incubation | ||
| 700 | 1 | |a Aidar, Moldazhanov |u Faculty of Engineering Technologies, Kazakh National Agrarian Research University, Almaty 050010, Kazakhstan; moldazhanov.a@kaznaru.edu.kz (A.M.); kulmakhambetova.akmaral@kaznaru.edu.kz (A.K.); azimjan.azizov@kaznaru.edu.kz (A.A.) | |
| 700 | 1 | |a Akmaral, Kulmakhambetova |u Faculty of Engineering Technologies, Kazakh National Agrarian Research University, Almaty 050010, Kazakhstan; moldazhanov.a@kaznaru.edu.kz (A.M.); kulmakhambetova.akmaral@kaznaru.edu.kz (A.K.); azimjan.azizov@kaznaru.edu.kz (A.A.) | |
| 700 | 1 | |a Azizov Azimjan |u Faculty of Engineering Technologies, Kazakh National Agrarian Research University, Almaty 050010, Kazakhstan; moldazhanov.a@kaznaru.edu.kz (A.M.); kulmakhambetova.akmaral@kaznaru.edu.kz (A.K.); azimjan.azizov@kaznaru.edu.kz (A.A.) | |
| 700 | 1 | |a Zhassulan, Otebayev |u Faculty of Veterinary Medicine and Animal Science, Kazakh National Agrarian Research University, Almaty 050010, Kazakhstan; shokhan.alpeisov@kaznaru.edu.kz (S.A.); otebayev.zhassulan@kaznaru.edu.kz (Z.O.) | |
| 700 | 1 | |a Zinchenko Dmitriy |u Faculty of Engineering Technologies, Kazakh National Agrarian Research University, Almaty 050010, Kazakhstan; moldazhanov.a@kaznaru.edu.kz (A.M.); kulmakhambetova.akmaral@kaznaru.edu.kz (A.K.); azimjan.azizov@kaznaru.edu.kz (A.A.) | |
| 773 | 0 | |t AgriEngineering |g vol. 7, no. 12 (2025), p. 411-431 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286238586/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286238586/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286238586/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |