Early Detection of Lameness in Cattle with Image Processing Techniques

Abstract

Lameness in dairy cattle has a negative effect on fertility, milk yield and various behaviors. Therefore, lameness in cattle causes significant economic losses in countries. In our article, it is aimed to detect lameness in cattle early with image processing techniques. Deep learning and image processing techniques were used in the article. In the article, YOLOv5 algorithm is used for object detection and Shufflenetv2k30 algorithm is used as image processing technology. Within the scope of the article, the images were subjected to a preprocessing (data augmentation) and then the cattle in the selected photos were identified by our trained deep learning model. The detected cattle were tagged and then the posture estimation of these tagged cattle was made. The angles between the joints of the cattle were found on the cattle whose posture pose was estimated. In the performance analysis, training was started with the weights of the Pre-training yolov5l model and the best weight output of the 200 epoch trained model was 75%. The best weight output of the model trained from zero to 400 epochs without using any model weights was 63%. Pre-training was started with the weights of the shufflenetv2k30 model and the weight output of the model trained for 400 epochs was 71%. This article will contribute to the studies to be done in the academic field and will create important data for the studies to be done in the livestock sector.