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.

中文翻译：

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基于深度学习的猪采食行为自动识别

已经开发出基于视觉的自动化早期预警系统来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、遮挡和不同猪的相似外观等问题，饲喂行为的自动记录仍然是一个挑战。此外，由于无法识别和/或排除对饲养区的非营养访问 (NNV)，依赖于猪跟踪的此类系统通常会高估实际饲养时间。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）不依赖猪跟踪，（b）能够区分一组猪的喂食和 NNV。我们首先使用商业养猪场的视频片段验证了我们的方法，在各种设置下。我们证明了这种自动化方法能够以高精度 (99.4% ± 0.6%) 识别喂养和 NNV 行为。然后，我们测试了该方法在计划的食物限制期间检测喂养和 NNV 行为变化的能力。我们发现该方法能够自动量化喂养和 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圈养猪群的饲养行为，而无需额外的传感器或单独的标记。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。然后，我们测试了该方法在计划的食物限制期间检测喂养和 NNV 行为变化的能力。我们发现该方法能够自动量化喂养和 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圈养猪群的饲养行为，而无需额外的传感器或单独的标记。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。然后，我们测试了该方法在计划的食物限制期间检测喂养和 NNV 行为变化的能力。我们发现该方法能够自动量化喂养和 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圈养猪群的饲养行为，而无需额外的传感器或单独的标记。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。我们的方法能够稳健而准确地监测商业圈养猪群的饲养行为，而无需额外的传感器或单独的标记。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。我们的方法能够稳健而准确地监测商业圈养猪群的饲养行为，而无需额外的传感器或单独的标记。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。