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Extracting spatial association rules in remotely sensed data of yellow rust disease in wheat crop at Udham Singh Nagar

机译:在Udham Singh Nagar的小麦作物黄锈病遥感数据中提取空间关联规则

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To ascertain wheat yield losses caused by stripe rust present work deals with the development of computer intelligent system, that uses improved Apriori algorithm to extract spatial association rules from spatial databases which helps in a decision support system for wheat crop yellow rust disease management. The approach aims to extract interesting frequent patterns and spatial associations among the used spatial databases that were obtained after the preprocessing and analysis of data using ENVI and Arc GIS tools. Firstly spatial data and their relationships were abstracted into generic database and then finally the file was used to extract spatial association rules. The implementation was successfully done using Java. Spatial association rules obtained have been verified by agriculture domain experts and the used algorithm has been found to be better.
机译:为了确定由条锈病引起的小麦产量损失,目前的工作涉及计算机智能系统的开发,该系统使用改进的Apriori算法从空间数据库中提取空间关联规则,这有助于建立小麦作物黄锈病管理的决策支持系统。该方法旨在在使用ENVI和Arc GIS工具对数据进行预处理和分析之后,在使用的空间数据库之间提取有趣的频繁模式和空间关联。首先将空间数据及其关系抽象到通用数据库中,然后使用该文件提取空间关联规则。使用Java成功完成了实现。获得的空间关联规则已通过农业领域专家的验证,并且发现使用的算法更好。

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