| 基于粒子群优化算法的土壤养分管理分区 |
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| 引用本文： | 王子龙,付 强,姜秋香.基于粒子群优化算法的土壤养分管理分区[J].农业工程学报,2008,24(10):80-84. |
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| 作者姓名： | 王子龙 付 强 姜秋香 |
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| 作者单位： | 东北农业大学水利与建筑学院,哈尔滨,150030 |
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| 基金项目： | 国家自然科学基金，黑龙江省科技攻关计划 |
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| 摘 要： | 土壤养分管理分区的划分为变量施肥技术提供依据,是精准农业实施变量施肥管理的重要环节。在对7种土壤养分的空间变异特征和变异结构进行分析的基础上,以其中6种养分作为变量进行土壤养分管理分区的研究,最后用基于粒子群优化属性均值聚类来划分管理分区,并引入3种指标确定合理的分区数目。通过计算得出,试验区的合理分区数目为2个,对各管理分区实际采样点的土壤养分数据进行单因素方差分析,除速效磷外各土壤养分均在99%的置信水平上具有极显著差异,其中分区2土壤肥力水平较高,分区1较低。基于粒子群优化属性均值聚类算法可以很好地划分土壤养分管理分区,分区结果能够为精准农业变量施肥提供决策依据。
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| 关 键 词： | 粒子群 属性均值聚类 管理分区 精准农业 |
| 收稿时间： | 2007/7/19 0:00:00 |
| 修稿时间： | 2008/8/28 0:00:00 |
Soil nutrient management zones based on particle swarm optimization algorithm |
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| Wang Zilong,Fu Qiang and Jiang Qiuxiang.Soil nutrient management zones based on particle swarm optimization algorithm[J].Transactions of the Chinese Society of Agricultural Engineering,2008,24(10):80-84. |
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| Authors: | Wang Zilong Fu Qiang and Jiang Qiuxiang |
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| Institution: | College of Water Conservancy and Architecture,Northeast Agricultural University,Harbin 150030,China,College of Water Conservancy and Architecture,Northeast Agricultural University,Harbin 150030,China and College of Water Conservancy and Architecture,Northeast Agricultural University,Harbin 150030,China |
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| Abstract: | Delineation of soil nutrient management zones provides basis for variable fertilization technique and it is an important link of variable fertilization management of precision agriculture.On the basis of analyzing spatial variability characteristics and structure of seven soil nutrients,six of them were taken as the variables to delineate soil nutrient management zones.Finally,attribute means clustering algorithm optimized by particle swarm optimization was used to delineate management zones,and three indices were introduced to ascertain the reasonable number of management zones.According to the calculation,the reasonable number of management zones for the study area was two.The results of variance analysis of single factor soil nutrient data of the practical samples in each management zone indicated that all soil nutrients but available phosphorus had great differences among management zones at the confidence level of 99%.Zone 2 had the higher soil fertility and Zone 1 had the lower.Final results show that attribute means clustering algorithm optimized by particle swarm optimization can be utilized to delineate management zones effectively.The defined management zones can provide decision-making support for variable fertilization of precision agriculture. |
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| Keywords: | particle swarm optimization attribute means clustering management zone precision agriculture |
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