Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada.
Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada.
J Anim Sci. 2021 Feb 1;99(2). doi: 10.1093/jas/skab022.
Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.
监测、记录和预测牲畜体重（BW）可以及时干预饮食和健康，提高遗传选择效率，并确定最佳的上市时间，因为已经达到屠宰点的动物对饲养场来说是一种负担。目前，有两种主要的方法（直接和间接）来测量牲畜的 BW。直接方法包括部分重量或全重量工业秤，放置在大型农场的指定位置，被动或动态地测量牲畜的重量。虽然这些设备非常精确，但它们的获取、预期用途和操作规模、与在高温变化和腐蚀性环境中放置相关的重复校准和维护成本，以及它们的高成本超出了中小型农场甚至商业运营商的承受能力和可持续性限制。作为直接称重方法的更经济实惠的替代方案，已经开发出基于对牲畜生物计量和形态测量与 BW 之间的观察或推断关系的间接方法。最初的间接方法涉及使用卷尺和管手动测量动物，并使用能够将这些测量与 BW 相关联的回归方程。虽然这些方法具有良好的 BW 预测准确性，但它们耗时、需要经过培训和熟练的农场工人，并且对动物和处理人员都可能产生压力，尤其是当每天重复进行时。随着非接触式光电传感器（例如 2D、3D、红外摄像机）、计算机视觉（CV）技术以及机器学习（ML）和深度学习（DL）等人工智能领域的协同发展，2D 和 3D 图像已开始被用作 BW 估计的生物计量和形态计量代理。本文综述了基于 CV 和基于 ML/DL 的 BW 预测方法，并讨论了它们的优缺点和行业适用性潜力。
