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Decision Support System Cattle Weight Prediction using Artificial Selected Weighting Method

机译：人工选择加权法的决策支持系统牛体重预测

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Animal agriculture in Indonesia is a very important need. Beef production in Indonesia experienced fluctuations from 2015 to 2019. In that period, 2016 reached its highest point with 518,484 tons. Even so, the use of technology for animal husbandry is needed to help farmers improve the quality and quantity of livestock products. One of the needs of farmers is cattle scales. This scale is expensive so not all farmers can afford this. They weighing cattle by bringing it to the cattle market or a place that has been facilitated by the government. This causes farmers have to require transportation just only weighing cows. Another way is approximating the weight, but this method can only be done by someone who has high experience. The aim of this paper to propose a system for predicting the weight of cattle using a novel artificial selected weighting method. This method is based on hybrid image processing results combined with Danish Schoorl, Schoorl Indonesia, Winter Europe/Scheiffer, and Winter Indonesia calculations. The initial stage is image processing, the results of the parameters obtained will be calculated using Denmark Schoorl, Schoorl Indonesia, Winter Europe/Scheiffer, and Winter Indonesia formula. Calculation obtained will be learned by comparing it with the actual weight of livestock. The results of this learning process obtained weights that can be used to predict cattle weight. The results show using an artificial selected weighting method, the accuracy of prediction can be increased.
机译：印度尼西亚的畜牧业是非常重要的需求。从2015年到2019年，印度尼西亚的牛肉产量出现了波动。在此期间，2016年达到了518484吨的最高点。即便如此，仍需要将技术用于畜牧业，以帮助农民提高畜产品的质量和数量。农民的需求之一是牛秤。这种规模的价格昂贵，因此并非所有农民都能负担得起。他们通过将牛带到牛市或政府提供便利的地方来称重牛。这导致农民仅需称重母牛就需要运输。另一种方法是估算体重，但是这种方法只能由经验丰富的人来完成。本文的目的是提出一种使用新型人工选择加权方法预测牛体重的系统。该方法基于混合图像处理结果，并结合了丹麦的Schoorl，印度尼西亚的Schoorl，欧洲的冬季/ Scheiffer和印度尼西亚的冬季计算。初始阶段是图像处理，将使用丹麦Schoorl，Schoorl Indonesia，Winter Europe / Scheiffer和Winter Indonesia公式计算获得的参数结果。通过将其与牲畜的实际体重进行比较，可以了解所获得的计算结果。该学习过程的结果获得了可用于预测牛体重的权重。结果表明，使用人工选择的加权方法可以提高预测的准确性。

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