RESUMO
In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.
RESUMO
The late gestation period is crucial for fetal growth and development, impacting swine enterprises' profitability. Various nutritional strategies have been explored to enhance reproductive performance in sows, but findings regarding birth weight and litter size have been inconsistent. This study investigated the effects of increased feeding allowance during the late gestation period on the reproductive performance and farrowing behavior of primiparous and multiparous sows. A total of 28 sows (Landrace × Yorkshire) were used in this experiment, and fed 2.50 kg/d or 3.50 kg/d from 84 days of gestation until farrowing. Farrowing behavior was monitored using a DeepEyesTM M3SEN camera. The data were analyzed using the 2 × 2 factorial within Statistical Analysis System (SAS, 2011, Version 9.3) software. The results indicated that regardless of the parity number, sows fed a high diet exhibited a numerical increase in the total number of born piglets and a significant increase in milk yield (p = 0.014) and piglet birthweight (p = 0.023). Backfat thickness loss was significantly higher in sows with a 2.50 kg feeding allowance (p = 0.022), and the total number of piglets born, live births, and litter size were numerically higher in sows fed 3.50 kg per day. Moreover, stillborn piglets, mortality rate, and re-estrus days were numerically lower in sows with a high feeding allowance. The diet and parity did not individually affect the average duration of farrowing and farrowing intervals. However, the duration of postural changes in sows after farrowing was significantly reduced (p = 0.012). The principal component analysis revealed 81.40% and 80.70% differences upon partial least-squares discriminant analysis. Therefore, increasing feeding allowance during the late gestation period, regardless of parity, could positively influence sows' reproductive performance and piglets' growth performance during the lactation phase.
RESUMO
Porcine respiratory disease complex is an economically important disease in the swine industry. Early detection of the disease is crucial for immediate response to the disease at the farm level to prevent and minimize the potential damage that it may cause. In this paper, recent studies on the application of artificial intelligence (AI) in the early detection and monitoring of respiratory disease in swine have been reviewed. Most of the studies used coughing sounds as a feature of respiratory disease. The performance of different models and the methodologies used for cough recognition using AI were reviewed and compared. An AI technology available in the market was also reviewed. The device uses audio technology that can monitor and evaluate the herd's respiratory health status through cough-sound recognition and quantification. The device also has temperature and humidity sensors to monitor environmental conditions. It has an alarm system based on variations in coughing patterns and abrupt temperature changes. However, some limitations of the existing technology were identified. Substantial effort must be exerted to surmount the limitations to have a smarter AI technology for monitoring respiratory health status in swine.
