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Short-Term Prediction of Honey Production in Bosnia and Herzegovina using IoT

机译:使用物联网和黑塞哥维那在波斯尼亚和黑塞哥维那的蜂蜜生产短期预测

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This study presents a short-term prediction approach for honey production using ensemble regression technique. The data were recorded as a part of Habeetat project in Sarajevo, Bosnia and Herzegovina for 2016 season. This season has been entitled as one of the worst seasons for beekeepers in our country, which makes the problem of honey production prediction even more challenging. Random Tree regression algorithm was used for such purpose showing that the mean absolute error in predicting total honey production was less than 1.16 kg in all three hives monitored between November 2016 and April 2017. These findings are very significant for beekeepers since they can be notified in advance to visit individual hives and collect the honey. Besides, they can monitor trends in honey production throughout the season and perhaps change the position of hives in the current season and for the next upcoming season.
机译:本研究介绍了使用集合回归技术的蜂蜜生产的短期预测方法。该数据被记录为萨拉热窝,波斯尼亚和黑塞哥维那的Habeetat项目的一部分,2016年赛季。本赛季被题为我国养蜂人最糟糕的季节之一,这使得蜂蜜生产预测的问题更具挑战性。随机树回归算法用于此目的,显示预测总蜂蜜产量的平均绝对误差在2016年11月至2017年11月间监测的所有三个荨麻疹中的平均绝对误差小于1.16公斤。这些发现对于养蜂人来说非常重要,因为它们可以通知他们前进,参观个体荨麻疹并收集蜂蜜。此外,他们可以在整个季节监测蜂蜜生产的趋势,也许可能会改变当前季节的荨麻疹的位置,并为下一个即将到来的季节改变。

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