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.

中文翻译：

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ASAS-NANP 研讨会：机器学习在数字图像中预测牲畜体重的应用

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，并确定上市动物的最佳时间，因为已经达到屠宰点的动物对畜牧业来说是一种负担饲养场。目前有两种主要方法（直接和间接）来测量牲畜的体重。直接方法包括放置在大型农场指定位置的部分重量或全重量工业秤，用于被动或动态测量牲畜的重量。虽然这些设备非常准确，但它们的采购、预期用途和操作尺寸、重复校准和维护成本与它们在高温变化中的放置相关，和腐蚀性环境非常重要，超出了中小型农场甚至商业经营者的承受能力和可持续性限制。作为直接称重方法的一种更实惠的替代方法，已根据观察或推断的牲畜生物特征和形态测量与其体重之间的关系开发了间接方法。最初的间接方法包括使用卷尺和试管对动物进行手动测量，以及使用能够将此类测量与体重相关联的回归方程。虽然这些方法具有良好的 BW 预测准确度，但它们非常耗时，需要训练有素且技术熟练的农场工人，并且可能会给动物和处理者带来压力，尤其是在每天重复时。随着非接触式光电传感器（例如 2D、3D、红外相机）、计算机视觉 (CV) 技术和人工智能领域，如机器学习 (ML) 和深度学习 (DL)，2D 和 3D 图像已开始用作 BW 估计的生物特征和形态测量代理。本手稿回顾了基于 CV 和基于 ML/DL 的 BW 预测方法，并讨论了它们的优势、劣势和行业应用潜力。