湖北农业科学 ›› 2023, Vol. 62 ›› Issue (4): 185-189.doi: 10.14088/j.cnki.issn0439-8114.2023.04.033

• 信息工程 • 上一篇    下一篇

基于无监督过滤式指标选择的冬小麦种植区域尺度管理分区算法

万青松1, 罗晓姣2   

  1. 1.成都文理学院,成都 610499;
    2.四川锦兴国试书业有限公司,成都 610041
  • 收稿日期:2022-05-20 出版日期:2023-04-25 发布日期:2023-05-12
  • 通讯作者: 罗晓姣(1988-),女,四川资阳人,硕士,主要从事农业信息化、风景园林、休闲农业等研究,(电子信箱)wqs963111@126.com。
  • 作者简介:万青松(1981-),男,四川广汉人,助理研究员,硕士,主要从事农业信息化、农业管理、新媒体运营管理、乡村振兴等研究,(电话)18162561162(电子信箱)wls111222@126.com。

A regional scale management partition algorithm for winter wheat planting based on FSCC

WAN Qing-song1, LUO Xiao-jiao2   

  1. 1. Chengdu College of Arts and Sciences, Chengdu 610499, China;
    2. Sichuan Jinxing Guoshi Book Industry Co., Ltd., Chengdu 610041, China
  • Received:2022-05-20 Online:2023-04-25 Published:2023-05-12

摘要: 无监督过滤式指标选择(FSCC)对冬小麦种植区域尺度管理有直接影响。以重庆市某区域为研究对象,分析了无监督过滤式指标对冬小麦种植区域尺度管理分区精度的影响。首先,结合聚类算法建立数据样本库,研究冬小麦种植区域空间分布特征,同时计算区域精度、平均区域精度、区域精度标准差、均方根误差和偏差;然后,选取特征子集,实现数据分区计算。结果表明,无监督过滤式指标对冬小麦种植区域尺度管理分区精度的影响较大。在实现分区管理过程中,需要同时考虑无监督过滤式指标、空间范围、农作物种类和冬小麦种植密度4个因素,通过互相调节,确保分区效果达到最佳,从而提高冬小麦的种植产量。

关键词: 无监督过滤式指标选择(FSCC), 冬小麦, 种植区域, 尺度管理, 算法

Abstract: Feature selection based on correlation clustering algorithm(FSCC) has a direct impact on the scale management of winter wheat planting areas. Taking a certain area in Chongqing City as the research object, the impact of FSCC on the accuracy of winter wheat planting area scale management zoning was analyzed. Firstly, a data sample library was established using clustering algorithms to study the spatial distribution characteristics of winter wheat planting areas, while calculating regional accuracy, average regional accuracy, standard deviation of regional accuracy, root mean square error, and deviation. Then, a subset of features was selected to achieve data partitioning calculation. The results showed that FSCC had a significant impact on the accuracy of regional scale management zoning for winter wheat cultivation. In the process of implementing zoning management, it is necessary to simultaneously consider four factors: FSCC, spatial monitoring range, crop types, and planting density of winter wheat. Through mutual adjustment, it was ensured that the zoning effect was optimal, thereby improving the planting yield of winter wheat.

Key words: feature selection based on correlation clustering algorithm(FSCC), winter wheat, planting area, scale management, algorithms

中图分类号: