Artificial Intelligence in Agricultural Applications

LIU Xian; ZHENG Hui-yong; SHI Neng-qiang; LIU Yu-mei; LIN Ying-zhi

doi:10.19303/j.issn.1008-0384.2013.06.021

Volume 28 Issue 6

Jun. 2013

Turn off MathJax

Article Contents

Article Navigation > Fujian Journal of Agricultural Sciences > 2013 > 28(6): 609-614

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

Citation:

PDF( 878 KB)

Artificial Intelligence in Agricultural Applications

doi: 10.19303/j.issn.1008-0384.2013.06.021

Institute of digital agriculture,Fujian academy of agricultural sciences,Fuzhou,Fujian 350003,China

Received Date: 2013-04-01
Publish Date: 2013-06-18

Abstract

Abstract

Artificial intelligence is the forefront of the 21st Century technology development.Using the computer and control sciences, significant social and economic benefits have been realized.Its application to improve the production efficiency and management automation has become an essential task for the agricultural professionals as well.In China, the progress is seen crucial for the modernization and sustainability of its agriculture, and the continual improvements on the high-yield, high-efficiency and high-quality crops.
- Artificial intelligence,
- agriculture,
- application

FullText(HTML)

References(29)

References

[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.