基于模糊专家系统的精准农业分区产量期望图

W. Mirschel, K. Wenkel, R. Wieland, J. Bobert
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引用次数: 0

摘要

制定有助于确定农田内管理单位实际产量预期的程序是一项科学挑战。为此,开发了一个适用于场景的模糊专家系统。生成特定地点的产量预期图的程序分为三个有条不紊的步骤——步骤1:估计产量潜力;步骤2:作物种植前和作物品种的影响;步骤3:特定地点参数的影响。区域方法考虑到物理参数以及气象输入数据或品种试验结果,以预测区域平均产量。这个预测是加入特定站点模糊模型的起点。在估算产量时,考虑了影响产量形成的区位异质性参数的空间信息。利用土壤物理参数(植物有效水分、潜在毛管上升、地下水位、地形属性)生成场地特定产量期望图。这张地图是使用空间分析和建模工具(SAMT)生成的。描述了一种训练模糊模型的方法。2000年和2005年,在东德萨克森-安哈尔特的Chernozem地区的一块种植冬小麦的45公顷土地上对该方法进行了试验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Zonal yield expectation maps for Precision Agriculture generated using a combined Fuzzy-Expert system
The development of procedures which facilitate the determination of realistic yield expectations for management units within agricultural fields represents a scientific challenge. For this an applicable and scenario-apt Fuzzy-Expert system was developed. The procedure for generation of sites-specific yield expectation maps is divided into three methodical steps – step 1: estimation of yield potential, step 2: influences of pre-crop and crop variety and step 3: influence of site-specific parameters. The regional approach takes into account physical parameters as well as meteorological input data or results of variety testing trials for prediction of regional average yields. This prediction is starting point for the joined site-specific fuzzy model. For yield estimation site-specific heterogeneity spatial information of parameter influencing yield formation are taken into account. Physical soil parameter (plant available water, potential capillary rise, ground water table, landform attributes) are used to generate the site specific yield expectation map. This map is generated using the Spatial Analysis and Modeling Tool (SAMT). A method for training of the fuzzy model is described. The procedure was tested on a 45 ha field cropped with winter wheat in a Chernozem area of Saxony-Anhalt in East-Germany for the years 2000 and 2005.
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