• 中文核心期刊
  • CSCD来源期刊
  • 中国科技核心期刊
  • CA、CABI、ZR收录期刊

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

人工智能在农业生产中的应用进展

刘现 郑回勇 施能强 刘玉梅 林营志

刘现, 郑回勇, 施能强, 刘玉梅, 林营志. 人工智能在农业生产中的应用进展[J]. 福建农业学报, 2013, 28(6): 609-614. doi: 10.19303/j.issn.1008-0384.2013.06.021
引用本文: 刘现, 郑回勇, 施能强, 刘玉梅, 林营志. 人工智能在农业生产中的应用进展[J]. 福建农业学报, 2013, 28(6): 609-614. doi: 10.19303/j.issn.1008-0384.2013.06.021
LIU Xian, ZHENG Hui-yong, SHI Neng-qiang, LIU Yu-mei, LIN Ying-zhi. Artificial Intelligence in Agricultural Applications[J]. Fujian Journal of Agricultural Sciences, 2013, 28(6): 609-614. doi: 10.19303/j.issn.1008-0384.2013.06.021
Citation: LIU Xian, ZHENG Hui-yong, SHI Neng-qiang, LIU Yu-mei, LIN Ying-zhi. Artificial Intelligence in Agricultural Applications[J]. Fujian Journal of Agricultural Sciences, 2013, 28(6): 609-614. doi: 10.19303/j.issn.1008-0384.2013.06.021

人工智能在农业生产中的应用进展

doi: 10.19303/j.issn.1008-0384.2013.06.021
基金项目: 

福建省财政专项——福建省农业科学院科技创新团队建设项目 (CXTD-1-1310)

福建省科技创新平台建设项目 (2009J1002、2010J1002)

福建省农业科学院科技重大专项 (ZDZX-1302)

详细信息
    作者简介:

    刘现:林营志 (1974-) , 男, 博士, 研究方向:环境感知与智能控制

  • 中图分类号: S126;TP18

Artificial Intelligence in Agricultural Applications

  • 摘要: 本文综述了人工智能技术在农业生产中的应用现状。采用分阶段描述的方法分别详细阐述目前人工智能各种技术在农业生产的产前、产中和产后各阶段的应用情况, 总结人工智能在农业生产应用中的不足并展望其应用前景。由此可得, 随着人工智能技术的不断成熟, 利用人工智能技术提高农业生产的效率和农业生产管理的自动化水平越来越普遍, 人工智能将为我国发展高产、高效、优质、可持续的现代化农业做出巨大贡献。
  • [1] 廉师友.人工智能技术导论[M].西安:西安电子科技大学出版社, 2007.
    [2] 边肇祺, 张学工.模式识别[M].北京:清华大学出版社, 2002.
    [3] ODHIAMBO L O, FREELAND R S, YODER R E, et al.Investigation of a fuzzy-neural network application inclassification of soils using groundpenetrating radar imagery[J].Applied Engineering in Agriculture, 2004, 20 (1) :109-117.
    [4] COCKX L, VAN MEIRVENNE M, VITHARANA UWA, etal.Extracting topsoil information from EM38DD sensor datausing a neural network approach[J].Soil Science Society ofAmerica Journal, 2009, 73 (6) :2051-2058.
    [5] ELGAALI E, GARCIA L A, OJIMA D S.Sensitivity ofirrigation water balance to climate change in the great plains ofColorado[J].Transactions of the ASABE, 2006, 49 (5) :1315-1322.
    [6] RAJU K S, KUMAR D N, DUCKSTEIN L.Artificial neuralnetworks and multicriterion analysis for sustainable irrigationplanning[J].Computers&Operations Research, 2006, 33 (4) :1138-1153.
    [7] RASOULI K, HSIEH W W, CANNON A J.Daily streamflowforecasting by machine learning methods with weather andclimate inputs[J].Journal of Hydrology, 2012, 414-415:284-293.
    [8] RICHERT E, BIANCHIN S, HEILMEIER H, et al.Amethod for linking results from an evaluation of land usescenarios from the viewpoint of flood prevention and natureconservation[J].Landscape and Urban Planning, 2011, 103 (2) :118-128.
    [9] ZAPOTOCZNY P.Discrimination of wheat grain varieties usingimage analysis and neural networks[J].Journal of CerealScience, 2011, 54 (1) :60-68.
    [10] ORELLANA F J, DEL SAGRADO J, DELGUILA I M.SAIFA:A web-based system for Integrated Production ofolive cultivation[J].Computers and Electronics inAgriculture, 2011, 78 (2) :231-237.
    [11] LI Y S, HONG L F.Development of a Non-Pollution OrangeFruit Expert System Software Based on ASP.NET[J].Agricultural Sciences in China, 2011, 10 (5) :805-812.
    [12] NUTHALL P L.The intuitive world of farmers-The case ofgrazing management systems and experts[J].AgriculturalSystems, 2012, 107:65-73.
    [13] HUANG Y J, LEE F F.An automatic machine vision-guidedgrasping system for Phalaenopsis tissue culture plantlets[J].Computers and Electronics in Agriculture, 2010, 70 (1) :42-51.
    [14] NAKARMI A D, TANG L.Automatic inter-plant spacingsensing at early growth stages using a 3Dvision sensor[J].Computers and Electronics in Agriculture, 2012, 82:23-31.
    [15] ARRIBAS J I, SZALO V-FERRERO G V, RUIZ-RUIZ G, etal.Leaf classification in sunflower crops by computer visionand neural networks[J].Computers and Electronics inAgriculture, 2011, 78 (1) :9-18.
    [16] MIDTIBY H S, MATHIASSEN S K, ANDERSSON K J, etal.Performance evaluation of a crop/weed discriminatingmicrosprayer[J].Computers and Electronics in Agriculture, 2011, 77 (1) :35-40.
    [17] DONG F, HEINEMANN W, KASPER R.Development of arow guidance system for an autonomous robot for whiteasparagus harvesting[J].Computers and Electronics inAgriculture, 2011, 79 (2) :216-225.
    [18] PETTERSSON A, DAVIS S, GRAY J O, et al.Design of amagnetorheological robot gripper for handling of delicate foodproducts with varying shapes[J].Journal of FoodEngineering, 2010, 98 (3) :332-338.
    [19] PETTERSSON A, OHLSSON T, DAVIS S, et al.Ahygienically designed force gripper for flexible handling ofvariable and easily damaged natural food products[J].Innovative Food Science and Emerging Technologies, 2011, 12 (3) :344-351.
    [20] MOREDA G P, MUNOZ M A, RUIZ-ALTISENT M, et al.Shape determination of horticultural produce using two-dimensional computer vision-A review[J].Journal of FoodEngineering, 2012, 108 (2) :245-261.
    [21] LI Y Y, DHAKAL S, PENG Y.A machine vision system foridentification of micro-crack in egg shell[J].Journal of FoodEngineering, 2012, 109 (1) :127-134.
    [22] MATHANKER S K, WECKLER P R, BOWSER T J, et al.AdaBoost classifiers for pecan defect classification[J].Computers and Electronics in Agriculture, 2011, 77 (1) :60-68.
    [23] WANG S J, LIU K S, YU X J, et al.Application of hybridimage features for fast and non-invasive classification of raisin[J].Journal of Food Engineering, 2012, 109 (3) :531-537.
    [24] RODERO E, GONZáLEZ A, LUQUE M, et al.Classificationof Spanish autochthonous bovine breeds.Morphometric studyusing classical and heuristic techniques[J].LivestockScience, 2012, 143 (2-3) :226-232.
    [25] SINHA K, SAHA P D, DATTA S.Response surfaceoptimization and artificial neural network modeling ofmicrowave assisted natural dye extraction from pomegranaterind[J].Industrial Crops and Products, 2012, 37 (1) :408-414.
    [26] GHOSH A, TAMULY P, BHATTACHARYYA N, et al.Estimation of theaflavin content in black tea using electronictongue[J].Journal of Food Engineering, 2012, 110 (1) :71-79.
    [27] GORI A, CEVOLI C, FABBRI A, et al.A rapid method todiscriminate season of production and feeding regimen ofbutters based on infrared spectroscopy and artificial neuralnetworks[J].Journal of Food Engineering, 2012, 109 (3) :525-530.
    [28] LLAVE Y A, HAGIWARA T, SAKIYAMA T.Artificialneural network model for prediction of cold spot temperature inretort sterilization of starch-based foods[J].Journal of FoodEngineering, 2012, 109 (3) :553-560.
    [29] HUANG Y, LAN Y, THOMSON S J, et al.Development ofsoft computing and applications in agricultural and biologicalengineering[J].Computers and Electronics in Agriculture, 2010, 71 (2) :107-127.
  • 加载中
计量
  • 文章访问数:  292
  • HTML全文浏览量:  67
  • PDF下载量:  24
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-04-01
  • 刊出日期:  2013-06-18

目录

    /

    返回文章
    返回