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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

27

期数:

2017年12期

页码:

157-160

栏目:

应用开发研究

出版日期:

2017-12-10

文章信息/Info

Title:

Exploration on Chinese Rose Cut Flowers Diseases and Insect Pests Forecasting Model Based on Intelligent Algorithm

文章编号:

1673-629X(2017)12-0157-04

作者:

钱 晔1 2 ; 孙吉红3 ; 彭 琳1 2 ; 李文峰2 4 ; 汪惜今5 ; 陆国泉1 2

1. 云南农业大学 基础与信息工程学院,云南 昆明 650201;
2. 云南省高校农业信息技术重点实验室,云南 昆明 650201;
3. 云南省科学技术院,云南 昆明 650000;
4. 云南农业大学 新农村发展研究院,云南 昆明 650201;
5. 成都信息工程大学 大气科学学院,四川 成都 610000)

Author(s):

QIAN Ye 1 2 ; SUN Ji-hong 3 ; PENG Lin 1 2 ; LI Wen-feng 2 4 ; WANG Xi-jin 5 ; LU Guo-quan 1 2

1. School of Basis and Information Engineering,Yunnan Agricultural University,Kunming 650201,China;
2. Key Laboratory of Agricultural Information Technology in Yunnan Province,Kunming 650201,China;
3. Institute of Science and Technology in Yunnan,Kunming 650000,China;
4. New Rural Development Research Institute,Yunnan Agricultural University,Kunming 650201,China;
5. School of Atmosphere,Chengdu University of Information Technology,Chengdu 610000,China

关键词:

智能算法; 月季鲜切花病虫害; 自组织竞争神经网络; 预测

Keywords:

intelligent algorithm; fresh cut flowers plant diseases and insect pests; self-organizing competitive ANN; forecasting

分类号:

TP301

DOI:

10.3969/ j. issn.1673-629X.2017.12.034

文献标志码:

A

摘要:

针对传统预测模型的主观性强、成本偏高、误差偏大等问题,设计并提出了基于自组织竞争神经网络算法的月季
鲜切花病虫害预测模型。 该模型能够有效预防病虫害危害,确保月季鲜切花的正常生长,从而确保云南省月季鲜切花的
产量、质量和声誉。 以最为典型的月季鲜切花白粉病为实例,通过问卷调查、头脑风暴法相结合的加权方法来确定影响因子的权重,并以 60 组影响因子的数据作为输入数据,建立了基于自组织竞争神经网络算法的病虫害预测模型。 将所提出模型的预测结果与采用名义小组法所建立传统病虫害预测模型的预测结果进行对比分析。 实验结果及其分析表明,基于自组织竞争神经网络算法的预测模型可有效地为月季鲜切花种植企业、农户、散户提供更加准确的信息,降低了种植的盲目性。

Abstract:

In view of the problems of strong subjectivity,high cost and big error for traditional forecast model,based on self-organizing competitive ANN,the fresh cut flower plant diseases and insect pests forecasting model is designed. It can effectively prevent insect pests diseases from hazarding the normal growth of fresh cut flower and make sure the production,quality and reputation of fresh cut flowers in Yunnan Province. Taking most typical Chinese rose cut flowers powdery mildew for instance,the weights of impact factors are determined through questionnaire survey in combination with brainstorming,and then based on self-organizing competitive ANN,the forecast model
is to be built with 60 sets of impact factor as input of it. Compared with the traditional plant diseases and insect pests forecasting model,the experimental results show that the proposed model can provide effectively more accurate information for the fresh cut flowers enterprises,farmers,and retail investors to reduce their blindness of planting.

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更新日期/Last Update: 2018-03-07