Object Detection in Medical Diagnosis of Cows Using Convolutional Neural Network

Cattle farming is vital for global agriculture, supplying essentials like milk and meat. For both economic and animal welfare considerations, cow health is crucial. To diagnose cow health, this study uses cutting-edge deep learning methods including CNNs with Transfer Learning, Data Augmentation, Image Data Generator, and the Adam Optimizer. A variety of high-resolution cow picture were gathered for datasets and employ augmentation for generalization. Pre-trained CNNs are modified by transfer learning to identify a variety of diseases, such as Infectious Bovine Keratoconjunctivitis, Ringworm, Lumpy Disease, and Foot and Mouth Disease. The Adam optimizer speeds up model training, while the Image Data Generator effectively preprocesses data. Results show precise detection of cow ailments, showing the promise of deep learning in veterinary medicine. This research paper paves the way for useful, affordable automated methods for early cow health identification, which will help farmers and proponents of animal welfare. Overall, it shows the promise of deep learning in veterinary medicine for improved cow health and production throughout the world.