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Artificial Intelligence Applied to a Robotic Dairy Farm to Model Milk Productivity and Quality based on Cow Data and Daily Environmental Parameters

机译:人工智能应用于奶牛场机器人根据奶牛数据和每日环境参数对牛奶的生产率和质量进行建模

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摘要

Increased global temperatures and climatic anomalies, such as heatwaves, as a product of climate change, are impacting the heat stress levels of farm animals. These impacts could have detrimental effects on the milk quality and productivity of dairy cows. This research used four years of data from a robotic dairy farm from 36 cows with similar heat tolerance (Model 1), and all 312 cows from the farm (Model 2). These data consisted of programmed concentrate feed and weight combined with weather parameters to develop supervised machine learning fitting models to predict milk yield, fat and protein content, and actual cow concentrate feed intake. Results showed highly accurate models, which were developed for cows with a similar genetic heat tolerance (Model 1: n = 116, 456; R = 0.87; slope = 0.76) and for all cows (Model 2: n = 665, 836; R = 0.86; slope = 0.74). Furthermore, an artificial intelligence (AI) system was proposed to increase or maintain a targeted level of milk quality by reducing heat stress that could be applied to a conventional dairy farm with minimal technology addition.
机译:气候变化导致全球温度升高和气候异常(例如热浪)影响着农场动物的热应激水平。这些影响可能会对奶牛的牛奶质量和生产力产生不利影响。这项研究使用了来自机器人奶牛场的四年数据,该奶牛场来自36头具有类似耐热性的奶牛(模型1),以及来自该农场的所有312头奶牛(模型2)。这些数据由程序化的浓缩饲料和体重以及天气参数组成,以开发有监督的机器学习拟合模型,以预测牛奶产量,脂肪和蛋白质含量以及实际的母牛浓缩饲料摄入量。结果显示了高度准确的模型,这些模型是针对具有相似遗传耐热性的奶牛(模型1:n = 116、456; R = 0.87;坡度= 0.76)以及所有奶牛(模型2:n = 665,836; R)开发的= 0.86;斜率= 0.74)。此外,有人提出了一种人工智能(AI)系统,以通过减少热应力来提高或维持目标牛奶质量,该技术可以以最少的技术添加应用于传统的奶牛场。

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