Artificial Intelligence on Farms: Sheep Breed Classification Using Computer Vision (Indian Journal of Animal Production and Management, 2024)
As the challenges of population explosion,  food security and climate change become more  pressing, there is a dire need to produce more food.  Since animal husbandry is a crucial part of  agriculture, production per farm has to also  increase substantially over the next few years. This  is especially important for developing countries  where the introduction of technology and  automation can remove drudgery, improve  efficiency and reduce labour. An important part of  this is the use of images for automatic animal  monitoring. This research was therefore undertaken  to classify four breeds using three artificial  intelligence algorithms; K-nearest neighbours,  Support Vector Machines and Convolutional Neural  Networks (CNNs) which were evaluated and  ranked. We thus developed three deployable  models for image classification among which pre trained CNNs had the highest accuracy of 0.90. We  conclude that image classification therefore is  useful for automatic animal classification and  monitoring. This would help improve the production  potential of farms through the automation of farm  tasks.
Keywords: CNN; KNN; SVM; image classification; sheep images