Harnessing Data Science for Healthy Herds: Deep Learning for Cattle Disease Detection

Precision livestock management has shown promise in maintaining the well-being and productivity of cow herds. Lumpy skin disease (LSD) is a global problem for cow populations that affects food security and results in large financial losses. It is imperative to detect LSD in herds early and take prompt action to stop its spread. In this research work, a thorough method for the automatic identification of LSD in cattle that uses deep learning techniques is presented. Here, the performance of five listed convolutional neural network (CNN) models has already been trained: DenseNet, ResNet, InceptionV3, VGG, and Xception, which are investigated in detecting LSD in cow images. These models are refined using a large-scale dataset collected from a veterinary clinic and Kaggle, which includes pictures of calves that show signs of LSD infection. Work exploits the pre-trained representations obtained by these models on general image recognition tasks and adapts them to the LSD detection task, specifically using transfer learning. A comparison study is carried out to evaluate these models’ accuracy and computational effectiveness. This work also looks into how resilient the models are to changes in lighting, environmental factors, and cow breeds.