Detection Of Lumpy Skin Disease in Cows Using Machine Learning Techniques

LSD is a very infectious viral disease affecting cattle, which is the main reason for the huge losses that are being made in the livestock industry. Traditional diagnostic methods always rely on autoimmune testing and microscopic examination, which are too costly, time-consuming, and are subject to human errors. Our research suggests the use of a machine learning- based methodology as a tool that can be used for the early detection and prediction of Lumpy Skin Disease in animals. A range of classification models were designed and assessed using a dataset consisting of clinical images and symptom- based records. It was also necessary to employ data preprocessing techniques, including augmentation and normalization to drive the models. A comparative analysis conducted with different algorithms, such as CNNs and random forest classifiers, showed that deep learning methods are more accurate in identifying infected animals. From the initial results, we can see that a feasible, fast, cheaper, and reliable system is presented, hence it can be used to help veterinarians and farmers in the early diagnosis of LSD. There are also the expectations of the future to increase the diversity of the study data and to integrate the system into mobile applications for field usage.