Feed weight estimation model for health monitoring of meat rabbits based on deep learning

With the development of precision livestock farming, non-contact health monitoring technology is particularly important in the breeding process. To help improve the management of the rabbit breeding industry, a remaining feed weight (RFW) estimation model was developed based on the image segmentation method. The model proposed in this study consisted of a feed instance segmentation neural network and feed weight estimation network. Feed instance segmentation neural network was based on the improved Mask Region-based Convolution Neural Network (Mask RCNN), the state-of-art image segmentation method, and the PointRend algorithm was used to replace the original network head. Through an adaptive subdivision strategy, the boundary points were segmented with fine details. Features were extracted from the segmentation results and used as the input of the feed weight estimation network based on the Back Propagation (BP) algorithm. The model was applied in rabbit breeding to explore the relationship between RFW and the mortality probability of meat rabbits. The model evaluation results showed that the Average Precision (AP) value of the feed instance segmentation neural network was 0.987, the Mean Pixel Accuracy (MPA) value was 0.985. The correlation coefficient of the feed weight estimation network was 0.97, the Mean Squared Error (MSE) was 208.3, and the Mean Absolute Error (MAE) was 10.51 g. The practical application results showed that the feed intake of the unhealthy meat rabbits would decrease significantly. When the RFW was more than 50% of feed quantity, the mortality probability of the rabbit was more than 85%; when the RFW was more than 65% of feed quantity, all the rabbits finally died in a short time. Therefore, there is a significant correlation between RFW and the mortality probability of rabbits, by which this proposed model can help farms to monitor the health of meat rabbits by predicting RFW.