Development of a Technology for Detecting Straining in Cows During Labor Using Deep Learning

Abstract

In the dairy farming sector, the need for efficient individual cow management via ICT has intensified owing to a declining workforce in the industry and a simultaneous increase in livestock numbers. To address challenges such as reducing night-time cow-monitoring hours for workers and preventing calving accidents, we developed a system capable of automatically detecting straining during labor (a key indicator of impending cow calving). Our approach involved collecting waveform data on cow movements from an unrestrained cow positioned on a sensor sheet. The collected data were subsequently analyzed using deep learning techniques. Employing a sample of 40 cows, veterinarians correlated data collected using the sensor sheet with those collected using a video camera, classifying the data into straining and other movements unrelated to calving. This curated dataset was then used to train a staining detection model using a convolutional neural network, the accuracy of which was verified. Consequently, we successfully used the staining detection model to predict cow calving with a high accuracy rate of > 95%.