|Table of Contents|

Construction of Cut Flower Industrial Platform Based on Intelligent Model Group

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2019年04
Page:
162-166
Research Field:
Publishing date:

Info

Title:
Construction of Cut Flower Industrial Platform Based on Intelligent Model Group
Author(s):
QIAN Ye1; 2; SUN Jihong3; PENG Lin1; 2; ZHANG Jianbo3; YE Dan1; 2
(1.School of Big Data (Information Engineering),Yunnan Agricultural University,Kunming,Yunnan 650201；2.Key Laboratory of Agricultural Information Technology in Yunnan,Kunming,Yunnan 650201；3.Science and Technology in Yunnan,Kunming,Yunnan 650000)
Keywords:
fresh cut flowers; platform; intelligent algorithm; model
PACS:
-
DOI:
10.11937/bfyy.20182110
Abstract:
In order to ensure the healthy and sustainable development of Yunnan fresh cut flower industry,strengthen the income of employees and expanding the strength of fresh cut production and sales enterprises,ensure the strength of the largest fresh cut flower production and auction base in Asia.Through detailed analysis of the demand for fresh cut flower industry platform,a variety of intelligent algorithms were used to establish prediction models for diseases and pests of fresh cut flowers,price prediction models for fresh cut flowers,early warning system for fresh cut flowers and prediction models for fresh cut flowers.The model group on the industrial platform of fresh cut flower and a new cut flower industrial platform based on intelligent algorithm group were built.The first,second and future vision of Yunnan′s cut flower industry were presented,to give full play to the big data technology,intelligent algorithm,management information systems in agricultural field,to provide reference for the construction of intelligent platform for the integration of multiple disciplines.

References:

[1]王淑娟.云南鲜切花产量连续23年全国第一 总产值463.7亿元[J].云南农业,2017(5):95.[2]周三多.管理学[M].北京：中国石化出版社，2010.[3]金帅军.基于GRNN神经网络的农作物虫害量预测系统设计[D].呼和浩特:内蒙古工业大学,2013.[4]李哲敏,许世卫,崔利国，等.基于动态混沌神经网络的预测研究：以马铃薯时间序列价格为例[J].系统工程理论与实践,2015,35(8):2083-2090.[5]魏明桦,郑金贵.基于改进BP神经算法的农产品价格预测模型的构建与实现[J].唐山师范学院学报,2014,36(2):66-68.[6]孙吉红,张丽莲,武尔维，等.基于智能算法的价格预测模型探究[J].计算机技术与发展,2014,24(11):107-110.[7]陈芳,楼文高.基于广义回归神经网络的蔬菜市场日价格预测[J].浙江农业学报,2015,27(7):1253-1258.[8]任伟宏.农产品市场价格预测方法探析[J].中国农学通报,2011,27(26):209-212.[9]LIU C J,WECHSLER H．Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition[J]．IEEE Transactions on Image Processing,2002,11(4):467-476．[10]QIAO L S,CHEN S C,TAN X Y.Sparsity preserving projections with applications to face recognition[J]．Pattern Recognition,2010,43(1):331-341．[11]PAPAJORGJI P J,PARDALOS P M.Software engineering techniques applied to agricultural systems:An object-oriented and UML approach(applied optimization)[M].New York:Springer Verlag,2005．[12]钱晔,孙吉红,彭琳,等.基于智能算法的月季鲜切花病虫害预测模型的探究[J].计算机技术与发展,2017,27(12):156-160.[13]钱晔,孙吉红,孙媛媛,等.农业大数据环境下的鲜切花行情监测系统设计[J].安徽农业科学,2018,15(57):185-187.[14]郑强,彭琳,邹秋霞,等.基于嵌入式Web服务器的远程温室监控系统设计[J].农机化研究,2013(11):84-87.[15]姜海燕,朱艳,徐焕良,等.作物模型资源构造平台(CMRCP)的构建研究[J].农业工程学报,2008,36(2):170-175.

Memo

Memo:
-
Last Update: 2019-03-26