Data Mining Techniques for Modelling the Influence of Daily Extreme Weather Conditions on Grapevine, Wine Quality and Perennial Crop Yield

2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks Pub Date : 2010-07-28 DOI:10.1109/CICSyN.2010.15

S. Shanmuganathan, P. Sallis, A. Narayanan

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引用次数: 10

Abstract

The influences of daily weather extremes, such as maximum/ minimum temperatures, humidity, and precipitation, are observable in perennial crop phenology that in turn determines the annual crop yield in quality and quantity. In viticulture, grapevine phenology determines the quality of vintage produced from the grapes apart from the best effects by winemaker. Following a brief review of current literature in this research domain, the paper describes a data mining approach being developed to data association modelling to depict dependency relationships between daily weather extremes, grapevine phenology and yield indicators using data from a vineyard in northern New Zealand and daily weather extremes logged at a nearby meteorology station. An artificial neural network algorithm was used to classify the data associations and the chi-square test was used to establish the degree of dependence between the related variable values. The initial results of the approach to daily maximum weather conditions show potential.

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模拟每日极端天气条件对葡萄、葡萄酒品质和多年生作物产量影响的数据挖掘技术

每日极端天气的影响，如最高/最低温度、湿度和降水，在多年生作物物候中是可观察到的，而这些物候又决定了每年作物产量的质量和数量。在葡萄栽培中，除了酿酒师的最佳效果外，葡萄的物候也决定了葡萄的品质。在对该研究领域的当前文献进行简要回顾之后，本文描述了一种数据挖掘方法，该方法正在开发数据关联建模，以利用新西兰北部葡萄园的数据和附近气象站记录的每日极端天气之间的依赖关系，葡萄藤物候和产量指标。采用人工神经网络算法对数据关联进行分类，并采用卡方检验建立相关变量值之间的依赖程度。接近每日最大天气条件的初步结果显示出潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks

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