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3D Vision for Precision Dairy Farming

机译:精准乳业的3D视觉

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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.
机译:3D视觉系统将在下一代畜牧业中发挥重要作用,因为它们在自动化畜牧任务(例如监视,放牧,喂养,挤奶和铺垫)中提供了传感功能。本文将回顾在Precision Dairy Farming中已经实现的3D计算机视觉系统和技术。这次审查将包括对飞行时间和立体视觉系统在农业应用中的适用性的评估,以及在各种用例中正在探索的计算机视觉算法类别的细分。这些用例包括必须与动物紧密安全地互动的挤奶机器人和自动驾驶汽车等机器人平台,以及可以识别奶牛并检测身体状况得分和运动得分等健康指标偏差的智能系统。在分析每个用例后,很明显,需要可以在不受限制的环境中运行并适应畜群特征,天气条件,农场布局和动物与机器人交互的不同场景的系统。考虑到这一要求,本文提出了在人工智能(即几何深度学习)的新兴研究领域中产生的技术的应用。

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