3D Vision for Precision Dairy Farming. Issue 30 (2019)

• Record Type: Journal Article
• Authors: O' Mahony, Niall; Campbell, Sean; Carvalho, Anderson; Krpalkova, Lenka; Riordan, Daniel; Walsh, Joseph
• Is Part Of: IFAC-PapersOnLine. Volume 52:Issue 30(2019)
• Page Start: 312
• Page End: 317
• Publication Date: 2019
• Subjects: Automation -- Robotics in Agriculture -- Sensing -- Automation in Animal Farming; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2019.12.555
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 3D vision systems will play an important role in next-generation dairy farming due to the sensing capabilities they provide in the automation of animal husbandry tasks such as the monitoring, herding, feeding, milking and bedding of animals. This paper will review 3D computer vision systems and techniques that are and may be implemented in Precision Dairy Farming. This review will include evaluations of the applicability of Time of Flight and Streoscopic Vision systems to agricultural applications as well as a breakdown of the categories of computer vision algorithms which are being explored in a variety of use cases. These use cases range from robotic platforms such as milking robots and autonomous vehicles which must interact closely and safely with animals to intelligent systems which can identify dairy cattle and detect deviations in health indicators such as Body Condition Score and Locomotion Score. Upon analysis of each use case, it is apparent that systems which can operate in unconstrained environments and adapt to variations in herd characteristics, weather conditions, farmyard layout and different scenarios in animal-robot interaction are required. Considering this requirement, this paper proposes the application of techniques arising from the emerging field of research in Artificial Intelligence that is Geometric Deep Learning.