Technological Tools and Artificial Intelligence in Estrus Detection of Sows-A Comprehensive Review

Simple Summary Accurate estrus detection by farmers is key to successful sow breeding as it offers the opportunity for timely mating, successful reproduction, and increased productivity. Traditional estrus detection methods based on physiological changes, mounting, and back pressure tests often fall short in accuracy, leading to lower conception rates and smaller litters. To address these issues, researchers are exploring modern technologies to study parameters such as vulvar temperature, posture, movement, and sound in relation to estrus. This review examines the effectiveness of these modern estrus detection techniques, and the findings indicate that they can enhance the accuracy of heat detection compared to conventional methods.Abstract 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.