Cattle external diseases like
Foot and Mouth Disease (FMD), Lumpy Skin Disease (LSD), and Infectious
Bovine Keratoconjunctivitis (IBK) are the most highly contagious
diseases around the world. Early diagnosis is crucial for controlling
these diseases. Traditional Convolutional Neural Networks is the most
used architecture in the state-of-the-art of image processing and
computer vision field. According to our knowledge, no other system for
cattle disease detection in the husbandry farm has been introduced by
using deep learning techniques. This proposed model referred to early
detect the most common external diseases using several CNN architectures
like conventional deep CNN, Inception-V3, and VGG-16 in the field of
deep learning. All necessary steps for performing the diseases detection
model are completely described in the paper, from the data collection
to the process and outcome. The proposed system is established to be
effective, acquiring results with 95% accuracy, which may reduce human
errors in the identification process and will be helpful to recognize
diseases for veterinarians and husbandry farmers.