Verhoeven Steven, Chantziaras Ilias, Bernaerdt Elise, Loicq Michel, Verhoeven Ludo, Maes Dominiek
Unit of Porcine Health Management, Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820, Merelbeke, Belgium.
Lintjeshof, Pannenweg 200, 6031 RK, Nederweert, The Netherlands.
Porcine Health Manag. 2023 Mar 15;9(1):9. doi: 10.1186/s40813-023-00303-3.
Good estrus detection in sows is essential to predict the best moment of insemination. Nowadays, a technological innovation is available that detects the estrus of the sow via connected sensors and cameras. The collected data are subsequently analyzed by an artificial intelligence (AI) system. This study investigated whether such an AI system could support the farmer in optimizing the moment of insemination and reproductive performance. M&M: Three Belgian sow farms (A, B and C) where the AI system was installed, participated in the study. The reproductive cycles (n = 6717) of 1.5 years before and 1.5 years after implementation of the system were included. Parameters included: (1) farrowing rate (FR), (2) percentage of repeat-breeders (RB), (3) farrowing rate after first insemination (FRFI) and (4) number of total born piglets per litter (NTBP). Also, data collected by the system were analyzed to describe the weaning-to-estrus interval (WEI), estrus duration (ED) and the number of inseminations used per estrus. This dataset included 2261 cycles, collected on farms B and C.
In farm A, all parameters significantly improved namely FR + 4.3%, RB - 3.75%, FRFI + 6.2% and NTBP + 1.06 piglets. In farm B, the NTBP significantly decreased with 0.48 piglets, but in this farm the insemination dose was too low (0.8 × 10 spermatozoa per dose). In farm C, only the NTBP significantly increased with 0.45 piglets after the implementation of the system. The WEI as determined by the system varied between 78 and 90 h, being 10-20 h shorter in comparison with the WEI as determined by the farmer. The ED, determined by the system ranged from 48 to 60 h, and was less variable as compared to the ED as assessed by the farmer. The mean number of inseminations per estrus remained similar over time in farm B whereas it decreased over time from approximately 1.6-1.2 in farm C.
The AI system can help farmers to improve the reproductive performance, assess estrus characteristics and reduce the number of inseminations per estrus. Results may vary between farms as many other variables such as farm management, genetics and insemination dose also influence reproductive performance.
母猪的良好发情检测对于预测最佳授精时刻至关重要。如今，有一种技术创新可通过连接的传感器和摄像头检测母猪的发情情况。随后，人工智能（AI）系统会对收集到的数据进行分析。本研究调查了这样的AI系统是否能帮助养殖户优化授精时刻并提高繁殖性能。
三个安装了AI系统的比利时母猪养殖场（A、B和C）参与了该研究。纳入了系统实施前1.5年和实施后1.5年的繁殖周期（n = 6717）。参数包括：（1）产仔率（FR），（2）返情母猪百分比（RB），（3）首次授精后的产仔率（FRFI）和（4）每窝总产仔数（NTBP）。此外，对系统收集的数据进行了分析，以描述断奶至发情间隔（WEI）、发情持续时间（ED）以及每次发情所用的授精次数。该数据集包括在B场和C场收集的2261个周期。
在A场，所有参数均显著改善，即FR提高了4.3%，RB降低了3.75%，FRFI提高了6.2%，NTBP增加了1.06头仔猪。在B场，NTBP显著减少了0.48头仔猪，但该场的授精剂量过低（每剂量0.8×10个精子）。在C场，系统实施后仅NTBP显著增加了0.45头仔猪。系统确定的WEI在78至90小时之间，与养殖户确定的WEI相比短10 - 20小时。系统确定的ED范围为48至60小时，与养殖户评估的ED相比变异性较小。B场每次发情的平均授精次数随时间保持相似，而C场则从约1.6次降至1.2次。
AI系统可帮助养殖户提高繁殖性能、评估发情特征并减少每次发情的授精次数。由于许多其他变量（如养殖场管理、遗传因素和授精剂量）也会影响繁殖性能，不同养殖场的结果可能会有所不同。
