PEGASE, INRAE, Institut Agro, Saint Gilles, France.
Institut Agro, Univ Rennes1, CNRS, INRIA, IRISA, Rennes, France.
J Anim Sci. 2023 Jan 3;101. doi: 10.1093/jas/skad337.
Precision feeding is a strategy for supplying an amount and composition of feed as close that are as possible to each animal's nutrient requirements, with the aim of reducing feed costs and environmental losses. Usually, the nutrient requirements of gestating sows are provided by a nutrition model that requires input data such as sow and herd characteristics, but also an estimation of future farrowing performances. New sensors and automatons, such as automatic feeders and drinkers, have been developed on pig farms over the last decade, and have produced large amounts of data. This study evaluated machine-learning methods for predicting the daily nutrient requirements of gestating sows, based only on sensor data, according to various configurations of digital farms. The data of 73 gestating sows was recorded using sensors such as electronic feeders and drinker stations, connected weight scales, accelerometers, and cameras. Nine machine-learning algorithms were trained on various dataset scenarios according to different digital farm configurations (one or two sensors), to predict the daily metabolizable energy and standardized ileal digestible lysine requirements for each sow. The prediction results were compared to those predicted by the InraPorc model, a mechanistic model for the precision feeding of gestating sows. The scenario predictions were also evaluated with or without the housing conditions and sow characteristics at artificial insemination usually integrated into the InraPorc model. Adding housing and sow characteristics to sensor data improved the mean average percentage error by 5.58% for lysine and by 2.22% for energy. The higher correlation coefficient values for lysine (0.99) and for energy (0.95) were obtained for scenarios involving an automatic feeder system (daily duration and number of visits with or without consumption) only. The scenarios including an automatic feeder combined with another sensor gave good performance results. For the scenarios using sow and housing characteristics and automatic feeder only, the root mean square error was lower with gradient tree boosting (0.91 MJ/d for energy and 0.08 g/d for lysine) compared with those obtained using linear regression (2.75 MJ/d and 1.07 g/d). The results of this study show that the daily nutrient requirements of gestating sows can be predicted accurately using data provided by sensors and machine-learning methods. It paves the way for simpler solutions for precision feeding.
精准饲养是一种策略，旨在尽可能接近每头动物的营养需求来提供饲料的数量和组成，其目的是降低饲料成本和减少环境损失。通常，母猪的营养需求是由营养模型提供的，该模型需要输入母猪和畜群的特征数据，但也需要对未来的分娩性能进行估计。在过去十年中，新的传感器和自动化设备（如自动给料器和饮水器）已在养猪场中得到开发，并产生了大量数据。本研究根据不同数字农场的配置，仅基于传感器数据，评估了用于预测妊娠母猪日常营养需求的机器学习方法。使用电子给料器和饮水站等传感器、连接的称重秤、加速度计和摄像机记录了 73 头妊娠母猪的数据。根据不同数字农场的配置（一个或两个传感器），将 9 种机器学习算法应用于各种数据集场景，以预测每头母猪的每日可代谢能量和标准化回肠可消化赖氨酸需求。将预测结果与用于妊娠母猪精准饲养的机制模型 InraPorc 模型的预测结果进行了比较。还评估了有无通常集成到 InraPorc 模型中的配种时的畜舍条件和母猪特征的情景预测。将畜舍和母猪特征添加到传感器数据中，使赖氨酸的平均绝对百分比误差提高了 5.58%，能量提高了 2.22%。对于赖氨酸（0.99）和能量（0.95），仅涉及自动给料系统（每日持续时间和访问次数，有无消耗）的场景获得了更高的相关系数值。包含自动给料器和另一个传感器的场景给出了良好的性能结果。对于仅使用母猪和畜舍特征以及自动给料器的场景，与使用线性回归（2.75 MJ/d 和 1.07 g/d）相比，梯度提升树（gradient tree boosting）的均方根误差（0.91 MJ/d 和 0.08 g/d）更低。本研究的结果表明，可以使用传感器和机器学习方法提供的数据准确预测妊娠母猪的日常营养需求。这为更简单的精准饲养解决方案铺平了道路。
