|Table of Contents|

Application Research of Big Data Platform for Vegetable Industry

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2020年20
Page:
154-162
Research Field:
Publishing date:

Info

Title:
Application Research of Big Data Platform for Vegetable Industry
Author(s):
SUN Xiang123WU Huarui123ZHU Huaji123GU Jingqiu123
(1.Beijing Research Center for Information Technology in Agriculture,Beijing 100097;2.National Engineering Research Center for Information Technology in Agriculture,Beijing 100097;3.Key Laboratory for Information Technologies in Agriculture,the Ministry of Agriculture and Rural Areas,Beijing 100097)
Keywords:
vegetablebig dataplatformservice
PACS:
-
DOI:
10.11937/bfyy.20200633
Abstract:
Vegetable industry chain links are complex,and there are many related factors.It was extremely difficult to accurately monitor,optimize control and manage scientifically.It was an effective way to support the modernization of vegetable industry in the future to use big data technology to realize the correlation analysis of all factors of vegetable industry.The study put forward a framework for the construction of large data platform for vegetable industry,and studied it from four aspects,large data sources for vegetable,collection and processing,mining and analysis,and platform services.Platform services were divided into basic services,application services and display services.Basic services provided various data adapting access and integration sharing components;display services provided visual methods to show data association and trends;application services provided data analysis services such as production early warning,processing cold chain logistics,market sales,industrial development,etc.The platform provides support for management decision-making of vegetable production,processing,trade,market circulation,consumption and other industrial links through big data,and promotes the modernization transformation and upgrading of vegetable industry in China.

References:

[1]孙忠富,杜克明,郑飞翔,等.大数据在智慧农业中研究与应用展望[J].中国农业科技导报,2013,15(6):63-71.[2]DENG M X,DI L P,HAN W G,et al.Web-service-based monitoring and analysis of global agricultural drought[J].Photogrammetric Engineering and Remote Sensing,2013,79(10):929-943.[3]KLEIN L J,MARIANNO F J,ALBRECHT C M,et al.PAIRS:A scalable geo-spatial data analytics platform[C].PAIRS:2015 IEEE Conference on Big Data (Big Data),2015.[4]王文生,郭雷风.农业大数据及其应用展望[J].江苏农业科学,2015,43(9):43-46.[5]DUTTA R,MORSHED A,ARYAL J,et al.Development of an intelligent environmental knowledge system for sustainable agricultural decision support[J].Environmental Modelling & Software,2014,52:264-272.[6]WOLFERT S,GE L,VERDOUW C,et al.Big data in smart farming:A review[J].Agricultural Systems,2017,153:69-80.[7]李瑾,顾戈琦.基于“互联网+”的农业大数据平台构建[J].湖北农业科学,2017,56(10):1947-1952.[8]邢芳.农产品冷链物流标准体系建设现状及对策[J].南方农业,2014,8(9):99-102.[9]AIGNER W,MIKSCH S,SCHUMANN H,et al.Visualization of time-oriented data[M].London:Human-computer Interaction Series,2011.[10]陈栋,吴保国,陈天恩,等.分布式多源农林物联网感知数据共享平台研发[J].农业工程学报,2017,33(s1):300-307.[11]米春桥,彭小宁,米允龙,等.农业大数据技术研究现状与发展趋势[J].安徽农业科学,2016,44(34):235-237.[12]李道亮,杨昊.农业物联网技术研究进展与发展趋势分析[J].农业机械学报,2018,49(1):1-20.[13]李涛,冯仲科,孙素芬,等.基于Hadoop的气象大数据分析GIS平台设计与试验[J].农业机械学报,2019,50(1):180-188.[14]STEINBRENER J,POSCHK,LEITNER R.Hyperspectral fruit and vegetable classification using convolutional neural networks[J].Computersand Electronicsin Agriculture,2019,162:364-372.[15]BARBEDO J G A.Plant disease identification from individual lesions and spots using deep learning[J].Biosystems Engineering,2019,180:96-107.[16]GEETHARAMANI G,ARUN PANDIAN J.Identification of plant leaf diseases using a nine-layer deep convolutional neural network[J].Computers & Electrical Engineering,2019,76:323-338.[17]王艳玲,张宏立,刘庆飞,等.基于迁移学习的番茄叶片病害图像分类[J].中国农业大学学报,2019,24(6):124-130.[18]张明岳,吴华瑞,朱华吉.基于卷积模型的农业问答语性特征抽取分析[J].农业机械学报,2018,49(12):203-210.[19]常英.基于机器视觉的樱桃番茄在线分级检测研究[D].兰州:兰州理工大学,2019.[20]SUAREZ-REY E M,GALLARDO M,ROMERO-GAMEZ M,et al.Sensitivity and uncertainty analysis in agro-hydrological modelling of drip fertigated lettuce crops under Mediterranean conditions[J].Computers and Electronics in Agriculture,2019,162:630-650.[21]PARK Y,NA M H,CHO W.Determination on environmental factors and growth factors affecting tomato yield using pattern recognition techniques[J].Multimedia Tools and Applications,2019,78(20):28815-28834.[22]张智,和志豪,洪婷婷,等.基于多层次模糊评判的樱桃番茄综合生长水肥耦合调控[J].农业机械学报,2019,50(12):278-287.[23]张仲雄,李斌,冯盼,等.基于植株需光差异特性的设施黄瓜立体光环境智能调控系统[J].智慧农业(中英文),2020,2(2):94-104.[24]段科俊,李再新.基于模糊自适应PID的农业温室系统研究[J].南方农机,2019,50(19):7-9.[25]杨银娟,鞠中安,曹婷婷,等.黄瓜霜霉病中期预警模型的研究.湖南农业大学学报(自然科学版),2019,45(4):444-448.[26]DOS SANTOS U J L,PESSIN G,DA COSTA C A,et al.AgriPrediction:A proactive internet of things model to anticipate problems and improve production in agricultural crops[J].Computers and Electronics in Agriculture,2019,161:202-213.[27]许世卫,李哲敏,李干琼,等.农产品市场价格短期预测研究进展[J].中国农业科学,2011,44(17):3666-3675.[28]陈为,张嵩,鲁爱东.数据可视化的基本原理与方法[M].北京:科学出版社,2013.[29]王勇,段玉聪,姜懿芮,等.设施蔬菜生产大数据挖掘及应用[J].中国瓜菜,2017,30(1):42-45,49.[30]姜懿芮,段玉聪,王勇,等.大数据在日光温室蔬菜生产中的应用[J].中国瓜菜,2019,32(1):42-44.

Memo

Memo:
-
Last Update: 2021-01-13