Estrus Detection Using Background Image Subtraction Technique in Tie-Stalled Cows.

In this study, we determined the applicability of the background image subtraction technique to detect estrus in tie-stalled cows. To investigate the impact of the camera shooting direction, webcams were set up to capture the front, top, and rear views of a cow simultaneously. Video recording was performed for a total of ten estrous cycles in six cows. Standing estrus was confirmed by testing at 6 h intervals. From the end of estrus, transrectal ultrasonography was performed every 2 h to confirm ovulation time. Foreground objects (moving objects) were extracted in the videos using the background subtraction technique, and the pixels were counted at each frame of five frames-per-second sequences. After calculating the hourly averaged pixel counts, the change in values was expressed as the pixel ratio (total value during the last 24 h/total value during the last 24 to 48 h). The mean pixel ratio gradually increased at approximately 48 h before ovulation, and the highest value was observed at estrus, regardless of the camera shooting direction. When using front-view videos with an appropriate threshold, estrus was detected with 90% sensitivity and 50% precision. The present method in particular has the potential to be a non-contact estrus detection method for tie-stalled cows.

An attempt at estrus detection in cattle by continuous measurements of ventral tail base surface temperature with supervised machine learning.

We aimed to determine the effectiveness of estrus detection based on continuous measurements of the ventral tail base surface temperature (ST) with supervised machine learning in cattle. ST data were obtained through 51 estrus cycles on 11 female cattle (six Holsteins and five Japanese Blacks) using the tail-attached sensor. Three estrus detection models were constructed with the training data (n = 17) using machine learning techniques (random forest, artificial neural network, and support vector machine) based on 13 features extracted from sensing data (indicative of estrus-associated ST changes). Estrus detection abilities of the three models on test data (n = 34) were not statistically different among models in terms of sensitivity and precision (range 50.0% to 58.8% and 60.6% to 73.1%, respectively). The relatively poor performance of the models might indicate the difficulty of separating estrus-associated ST changes from estrus-independent fluctuations in ST.