An Intelligent Edge-IoT Platform With Deep Learning for Body Condition Scoring of Dairy Cow

Body condition score (BCS) of dairy cows is the direct reflection of their nutritional status. The timely estimation of BCS is beneficial to improving dairy cow health, milk production, and reproduction. In this work, we propose an intelligent Edge-IoT platform with deep learning for estimating BCS of dairy cow, by integrating inference capability of deep learning and low latency of edge computing in IoT framework. Through capturing images of dairy cow’s back with the RGB-D camera, the inference module deployed in the edge computing device first performs cow detection to localize the separate area of each dairy cow and then performs individual identification and estimating BCS of dairy cows simultaneously. The existing systems are mainly commercial systems, such as DeLaval and HerdVision, they use electronic ear tags with radio-frequency identification sensors for cow identification. Compared to existing systems, in the proposed platform, combined the finetuned YOLOv7 model and avoid repeated inference (ARI) algorithm to detect dairy cow. An EfficientID model combined with metric learning is designed for cow identification, and an EfficientBCS model with coordinate attention (CA) is proposed for estimating BCS. The dairy cow’s identity (ID) and BCS are finally transmitted to the cloud analysis center. Experimental results show that the accuracy of estimating BCS reached 85% within 0.5 range error conducted on the test set collected in the dairy farm. The total inference time for one dairy cow is 3.138 s. Results show that the platform can be served as an excellent application of dairy cow body condition scoring.