A muzzle-based self-enhancing cow recognition method using YOLO and LLM

In the modern era, using the technology of image processing, people are being facilitated in many sectors. Therefore, we have decided to do research on cow recognition using the methods of image processing. Because cows are pretty essential for human beings, and people from the entire world are being facilitated by cows. Because of that reason, many technologies have been invented till now for the cow. Here we have presented an approach to identify the cow. Like the fingerprint of a human, the cow muzzle is also unique. Due to this reason, we can recognize the cow based on the muzzle. To recognize muzzles, we have used Yolo and the Structural Similarity Index (SSI). Image processing is a sector of Artificial Intelligence and data is quite important to train models. Moreover, to train a model for image processing, it is needed to manage a high-configuration computer. But there are many regions in the world where people hardly get such powerful machines. To mitigate this issue, we have introduced a unique method where we do not need a large amount of data for the image processing task. To eliminate the necessity of collecting a huge amount of data, we included a self- enhancing feature, which helps the model to predict accurately and also helps to improve performance by increasing the amount of data. However, without a large amount of data, it will be possible to predict appropriately because of the presence of self-enhancing features. This certain program helps the model to improve its capability for delivering faster responses. LLM is responsible to add the self-enhancing feature. On the other hand, to find out the most suitable Yolo model for our research, we have trained data with three different versions of Yolo: Yolo v7, v8, and v9. We evaluated the performances of these three versions in terms of Precision, Recall, mAP50, and mAP50-95 and found Yolo v9 to be the best among them. It excels with a precision of 0.391, a recall of 0.916, a mAP50 of 0.559, and a mAP50-95 of 0.307.