Abstract:
External cattle disorders such as Foot and Mouth Disease (FMD), Lumpy Skin Disease
(LSD), and Infectious Bovine Keratoconjunctivitis (IBK) are among the most common in
the sub-continent. Early detection is critical for disease control. The most widely utilized
architecture in the state-of-the-art of image processing and computer vision is the typical
convolutional neural network. No other method for detecting cattle diseases in a husbandry
farm has been implemented, leveraging deep learning techniques to our knowledge. This
suggested model uses different CNN architectures such as traditional deep CNN,
Inception-V3, and VGG-16 in the area of deep learning to identify the most prevalent
external illnesses at an early stage. The document details every step involved in conducting
the illness detection model, from data collection through procedure and result. The
suggested approach is successful, obtaining findings with a 95% accuracy rate, which may
help decrease human error during the classification and aid veterinarians and livestock
producers in recognizing diseases.
