Automatic recognition of feeding and foraging behaviour in pigs using deep learning
Automatic recognition of feeding and foraging behaviour in pigs using deep learning
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DOI：
10.1016/j.biosystemseng.2020.06.013
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发表时间：
2020-09-01
影响因子：
5.1
通讯作者：
Bacardit, Jaume
中科院分区：
文献类型：
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作者：
Alameer, Ali;Kyriazakis, Ilias;Bacardit, Jaume
自动化的、基于视觉的早期预警系统已经开发出来，用于检测猪群的行为变化，以监测它们的健康和福利状况。在商业环境中，由于光照变化、遮挡和不同猪的相似外观等问题，自动记录喂养行为仍然是一个挑战。此外，由于不能识别和/或排除对饲喂区域的非营养性访问（NNV），依赖于猪跟踪的这种系统经常高估饲喂所花费的实际时间。为了解决这些问题，我们开发了一种强大的、基于深度学习的喂食检测方法，该方法（a）不依赖于猪跟踪，（B）能够区分一组猪的喂食和NNV。我们首先使用来自商业养猪场的视频片段在各种设置下验证了我们的方法。我们证明了这种自动化方法以高准确度（99.4% +/- 0.6%）识别摄食和NNV行为的能力。然后，我们测试了该方法的能力，以检测喂养和NNV行为的变化，在计划的食物限制期间。我们发现，该方法能够自动量化摄食和NNV行为的预期变化。我们的方法能够稳健而准确地监测商业饲养的猪群的进食行为，而不需要额外的传感器或单独标记。这在早期检测商品猪的健康和福利挑战方面具有巨大的应用潜力。(C)2020作者（S）由Elsevier Ltd代表IAgrE出版。
Automated, vision-based early warning systems have been developed to detect behavioural changes in groups of pigs to monitor their health and welfare status. In commercial settings, automatic recording of feeding behaviour remains a challenge due to problems of variation in illumination, occlusions and similar appearance of different pigs. Additionally, such systems, which rely on pig tracking, often overestimate the actual time spent feeding, due to the inability to identify and/or exclude non-nutritive visits (NNV) to the feeding area. To tackle these problems, we have developed a robust, deep learning-based feeding detection method that (a) does not rely on pig tracking and (b) is capable of distinguishing between feeding and NNV for a group of pigs. We first validated our method using video footage from a commercial pig farm, under a variety of settings. We demonstrate the ability of this automated method to identify feeding and NNV behaviour with high accuracy (99.4% +/- 0.6%). We then tested the method's ability to detect changes in feeding and NNV behaviours during a planned period of food restriction. We found that the method was able to automatically quantify the expected changes in both feeding and NNV behaviours. Our method is capable of monitoring robustly and accurately the feeding behaviour of groups of commercially housed pigs, without the need for additional sensors or individual marking. This has great potential for application in the early detection of health and welfare challenges of commercial pigs. (C) 2020 The Author(s). Published by Elsevier Ltd on behalf of IAgrE.