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, inference module deployed in the edge computing device firstly 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 seconds. Results show that the platform can be served as an excellent application of dairy cow body condition scoring.

Reliable localization of the optic disc (OD) is important for retinal image analysis and ophthalmic pathology screening. This paper presents a novel method to automatically localize ODs in retinal fundus images based on directional models. According to the characteristics of retina vessel networks, such as their origin at the OD and parabolic shape of the main vessels, a global directional model, named the relaxed biparabola directional model, is first built. In this model, the main vessels are modeled by using two parabolas with a shared vertex and different parameters. Then, a local directional model, named the disc directional model, is built to characterize the local vessel convergence in the OD as well as the shape and the brightness of the OD. Finally, the global and the local directional models are integrated to form a hybrid directional model, which can exploit the advantages of the global and local models for highly accurate OD localization. The proposed method is evaluated on nine publicly available databases, and achieves an accuracy of 100% for each database, which demonstrates the effectiveness of the proposed OD localization method.