A review: Cattle Breed and Skin Disease Identification Using Deep Learning

In recent years, skin conditions have become a serious danger to livestock productivity, resulting in significant financial losses and health problems f or both humans and animals. Due to the complexity of this virus, it is difficult to detect, so early detection and accurate diagnosis are important. We thought a new approach for cattle skin disease detection, breed identification and weight estimation using image processing and deep learning techniques. The proposed system will use digital images taken from different angles of the cow's body to extract relevant features related to Lumpy skin disease, breed identification a nd weight prediction. So in this research paper we are providing a review on varieties of datasets, general architecture, different deep learning methods and performance metrics to be considered to implement such type of applications. Image processing techniques such as segmentation and texture analysis are used to isolate regions of interest and extract meaningful information about skin disease, body weight and breed. In this study further, we explore how to use Compared to conventional methods, convolutional neural networks which can detect and identify LSDV more quickly and precisely. We also considered some uses for this new technology and talked about the benefits of employing C NNs for this purpose. To determine the extent to which animals are affected by lumpy skin disease, we need to know the different stages of the disease that can be possible with the help of breed and weight identification. This review is concerned with Early diagnosis of the virus that causes lumpy skin condition, breed identification and weight prediction using multiple CNN methods and deep learning algorithms. These approaches have great potential to improve animal husbandry practices.