Violence Detection in Ranches Using Computer Vision and Convolution Neural Network

Terungwa Simon Yange (JS Tarka University Makurdi)
Charity Ojochogwu Egbunu (Department of Computer Science, Joseph Sarwuan Tarka University, Makurdi)
Oluoha Onyekware (Department of Computer Science, University of Nigeria, Nsukka)
Malik Adeiza Rufai (Department of Computer Science, Federal University Lokoja, Lokoja, Nigeria)
Comfort Godwin (Department of Computer Science, Joseph Sarwuan Tarka University, Makurdi)

Journal of Computer Scine and Information Technology
Volume 7 Issue 4 (2021): JCSITech

This study engaged the convolutional neural network in curbing losses in terms of resources that farmers spends in treating animals where injuries must have emancipated from violence among other animals and in worst case scenario could eventually lead to death of animals. Animals in a ranch was the target and the study proposed a method that detects and reports activities of violence to ranchers such that farmers are relieved of the stress of close supervision and monitoring to avoid violence among animals. The scope of the study is limited to violence detection in cattle, goat, horse and sheep. Different machine learning models were built for each animal. The models yielded good results; the horse violence detection model had an outstanding performance of 93% accuracy, 93% accuracy for the sheep model, 88% accuracy for the goat model and 84% accuracy for the cattle model.