Designing a Diagnostic Method to Predict the Optimal Artificial Insemination Timing in Cows Using Artificial Intelligence Provisionally Accepted

Megumi Nagahara¹ Satoshi Tatemoto² Takumi Ito¹ Otoha Fujimoto¹ Tetsushi Ono³ Masayasu Taniguchi³ Mitsuhiro Takagi³ Takeshige Otoi^4*

• ¹Bio-Innovation Research Center, Tokushima University, Tokushima, Japan, Japan
• ²Agricultural and Horticultural Research Division, Tokushima Prefectural Technical Support Center for Agriculture, Forestry and Fisheries, Tokushima, Japan, Japan
• ³Joint Faculty of Veterinary Science, Yamaguchi University, Yamaguchi 753-8515, Japan, Jamaica
• ⁴Tokushima University, Japan

Dairy farmers and beef cattle breeders aim for one calf per year to optimize breeding efficiency, relying on artificial insemination of both dairy and beef cows. Accurate estrus detection and timely insemination are vital for improving conception rates. However, recent challenges such as operational expansion, increased livestock numbers, and heightened milk production have complicated these processes. We developed an artificial intelligence (AI)-based pregnancy probability diagnostic tool to predict the optimal timing for artificial insemination. This tool analyzes external uterine opening image data through AI analysis, enabling high conception rates when inexperienced individuals conduct the procedure. In the initial experimental phase, images depicting the external uterine opening during artificial insemination were acquired for AI training. Static images were extracted from videos to create a pregnancy probability diagnostic model (PPDM). In the subsequent phase, an augmented set of images was introduced to enhance the precision of the PPDM. Additionally, a web application was developed for real-time assessment of optimal insemination timing, and its effectiveness in practical field settings was evaluated. The results indicated that when PPDM predicted a pregnancy probability of 70% or higher, it demonstrated a high level of reliability with accuracy, precision, and recall rates of 76.2%, 76.2%, and 100%, respectively, and an F-score of 0.86. This underscored the applicability and reliability of AI-based tools in predicting optimal insemination timing, potentially offering substantial benefits to breeding operations.