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首页> 外文期刊>Spanish Journal of Agricultural Research >Predictive modelling in grape berry weight during maturation process: comparison of data mining, statistical and artificial intelligence techniques.
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Predictive modelling in grape berry weight during maturation process: comparison of data mining, statistical and artificial intelligence techniques.

机译：成熟过程中葡萄浆果重量的预测模型：数据挖掘，统计和人工智能技术的比较。

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摘要

Environmental and geographical factors are two of the key aspects conditioning the growth of any crop, in such a way that the ability to predict significant variables of grape maturation can be highly useful to vine-growers. Berry weight is one of the variables monitored during this period, and the wineries have called for the development of an accurate prediction model. This study compares various types of data mining (DM) and artificial intelligence (AI) algorithms for developing an efficient prediction model for determining the variations in weight of grape berries during the ripening process according to the environmental and geographical properties not only throughout the ripening period but throughout the plant's cycle. The final objective is the search for a model that is efficient for data for new years with different properties to those in the past. This model helps the grower to harvest the grapes on the most suitable date for producing the best possible wine.Digital Object Identifier http://dx.doi.org/10.5424/http://dx.doi.org/10.5424/sjar/20110904-531-10
机译：环境和地理因素是限制任何作物生长的两个关键方面，因此预测葡萄成熟度的重要变量的能力对葡萄种植者非常有用。浆果重量是此期间监控的变量之一，酿酒厂已呼吁开发准确的预测模型。这项研究比较了各种类型的数据挖掘（DM）和人工智能（AI）算法，以开发一种有效的预测模型，该模型不仅可以根据整个成熟期的环境和地理特性，还可以根据其环境和地理特性来确定成熟期葡萄浆果的重量变化但在整个工厂周期中。最终目标是寻找一种模型，该模型对于具有不同属性的新年数据非常有效。该模型可帮助种植者在最合适的日期收获葡萄，以生产出最好的葡萄酒。数字对象标识符http://dx.doi.org/10.5424/http://dx.doi.org/10.5424/sjar/ 20110904-531-10

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