Abstract
Open grazing or free-range grazing is one of the methods employed by the Nigeria nomadic cattle herders to provide pasture for their cattle. This method of providing pasture for cattle comes with so many challenges among which are cow swapping, ownership disputes, rustling and cow intrusion to farmland. Some existing methods of guiding against these challenges are expensive, injurious, and unreliable to apply. The objective of this paper is to develop an enhanced and affordable software package for cow recognition and identification using a graphical user interface and information encoding method. Data analysis module with software application for the analysis of the generated code is proposed; the software application installed on a computer or smart-phone may be standalone or otherwise. Data about individual cow is digitally collected, coded and stored using necessary resources, tools, and methods. Moreover, by tagging individual cow with the generated code, and matching the code with the ones in the database using code reader, individual cow can be recognized and identified.Keywords: Open grazing; Free-range grazing; Nomadic herder; Cow identification; Pasture.
Highlights
Open grazing or free-range grazing is one of the methods employed by the Nigeria nomadic cattle herders to provide pasture for their cattle
The objective of this paper is to develop an enhanced and affordable software package for cow recognition and identification using a graphical user interface and information encoding method
Data analysis module with software application for the analysis of the generated code is proposed; the software application installed on a computer or smartphone may be standalone or otherwise
Summary
Open grazing or free-range grazing is one of the methods employed by the Nigeria nomadic cattle herders to provide pasture for their cattle. This nomadic method of grazing sometimes leads to cow getting strayed, rustled, swapped, and intruded to farmland causing disputes, destruction, and even death To address these challenges and many others, different animal recognition and identification methods have been proposed (Kumar et al, 2019; Kumar et al, 2018; Bello, 2018; Cheema and Anand, 2017). Apart from camera-trap, hand-designed, and sparse coding spatial pyramid matching (Yu et al, 2013) methods of extracting features for identification, many other recent recognition and identification projects that employed deep learning include Zin et al (2018) and Seijas et al (2019).
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