Estimation of beef marbling standard for live cattle using multi-input convolutional neural network with ultrasound images

There is substantial interest among livestock farmers in estimating the value of live beef cattle from ultrasound images. In this study, we aimed to clarify the knowledge employed for the estimation of beef marbling standard (BMS) by experts in visual inspection. To do this, we conducted a field survey, which revealed that the experts observe ultrasound images containing various body parts of live cattle: in particular, the loin part, iliocostalis part, and shoulder clod part. To automatically estimate the BMS value, we designed a convolutional neural network architecture that incorporates the experts' knowledge. We demonstrated that our multi-input neural network, with ultrasound images of various body parts, obtained a high accuracy in BMS estimation.