Review: Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of ''inexact'' computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed.

[1]  Stephen Grossberg,et al.  Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns , 1988, Other Conferences.

[2]  Steffen L. Lauritzen.  Highly Structured Stochastic Systems , 2003 .

[3]  Jacek M. Zurada,et al.  Computational Intelligence: Imitating Life , 1994 .

[4]  V. K. Jindal,et al.  Acoustic Testing of Snack Food Texture , 2001 .

[5]  Steven J. Thomson,et al.  Model-based irrigation management using a dynamic parameter adjustment method , 1996 .

[6]  Yibin Ying,et al.  Non-invasive determination of enological parameters of rice wine by Vis-NIR spectroscopy and least-squares support vector machines , 2008 .

[7]  David G. Stork,et al.  Pattern Classification , 1973 .

[8]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[9]  M. Pardo,et al.  Classification of electronic nose data with support vector machines , 2005 .

[10]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[11]  Leon S. Lasdon,et al.  Solving nonlinear water management models using a combined genetic algorithm and linear programming approach , 2001 .

[12]  András Fekete,et al.  Comparison of Quantitative Determination Techniques with Electronic Tongue Measurements , 2008 .

[13]  R. M. Lark,et al.  Approaches to Management Zone Definition for Use of Nitrification Inhibitors , 2003 .

[14]  Wei Wang,et al.  Prediction of Pork Meat Total Viable Bacteria Count Using Hyperspectral Imaging System and Support Vector Machines , 2008 .

[15]  Daniel C. Yoder,et al.  Optimization of Fuzzy Evapotranspiration Model Through Neural Training with Input-Output Examples , 2001 .

[16]  Christopher R. Houck,et al.  A Genetic Algorithm for Function Optimization: A Matlab Implementation , 2001 .

[17]  Suming Chen,et al.  Estimation of Leaf Nitrogen Content using Artificial Neural Network with Cross-Learning Scheme and Significant Wavelengths , 2007 .

[18]  J. W. Hines,et al.  INVESTIGATION OF A FUZZY-NEURAL NETWORK APPLICATION IN CLASSIFICATION OF SOILS USING GROUND-PENETRATING RADAR IMAGERY , 2004 .

[19]  Gerd Gigerenzer,et al.  How to Improve Bayesian Reasoning Without Instruction: Frequency Formats , 1995 .

[20]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[21]  Judea Pearl,et al.  Bayesian Networks , 1998, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[22]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[24]  Thomas Oommen,et al.  Analysis and Application of Support Vector Machine Based Simulation for Runoff and Sediment Yield , 2007 .

[25]  S. Prasher,et al.  Application of support vector machine technology for weed and nitrogen stress detection in corn , 2006 .

[26]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks for function approximation , 1993, IEEE International Conference on Neural Networks.

[27]  Randal K. Taylor,et al.  Effective Spatial Resolution for Weed Detection , 2007 .

[28]  M. Schaap,et al.  Neural network analysis for hierarchical prediction of soil hydraulic properties , 1998 .

[29]  Lakshmi,et al.  Auto calibration of complex watershed models using simulationoptimization framework , 2006 .

[30]  T. C. Pearson,et al.  DETECTION OF CORN KERNELS INFECTED BY FUNGI , 2006 .

[31]  P Dardenne,et al.  Classification of modified starches by fourier transform infrared spectroscopy using support vector machines. , 2005, Journal of agricultural and food chemistry.

[32]  Hans Hellendoorn.  Design and development of fuzzy systems at Siemens R&D , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[33]  Eugene Charniak,et al.  Bayesian Networks without Tears , 1991, AI Mag..

[34]  Marshall English,et al.  Optimizing Estimates of Soil Moisture for Irrigation Scheduling , 2008 .

[35]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[36]  L. P. Holmblad,et al.  CONTROL OF A CEMENT KILN BY FUZZY LOGIC , 1993 .

[37]  Robin Wardlaw,et al.  Application of genetic algorithms for irrigation water scheduling , 2004 .

[38]  Lotfi A. Zadeh,et al.  Possibility theory and soft data analysis , 1996 .

[39]  Noboru Noguchi,et al.  Path planning of an agricultural mobile robot by neural network and genetic algorithm , 1997 .

[40]  Kuang-Wen Hsieh,et al.  Neural network analysis of environmental conditions influencing cabbage seedling quality , 2003 .

[41]  Lieven Verbeke,et al.  Extracting topsoil information from EM38DD sensor data using a neural network approach , 2009 .

[42]  Michio Sugeno,et al.  Industrial Applications of Fuzzy Control , 1985 .

[43]  H. Ingleby,et al.  Neural network models for predicting organic matter content in Saskatchewan soils , 2001 .

[44]  Tomasz Markiewicz,et al.  Classification of milk by means of an electronic nose and SVM neural network , 2004 .

[45]  George E. Meyer,et al.  Digital Camera Operation and Fuzzy Logic Classification of Uniform Plant, Soil, and Residue Color Images , 2004 .

[46]  Ayse Irmak,et al.  Artificial Neural Network Model as a Data Analysis Tool in Precision Farming , 2006 .

[47]  Ward Edwards,et al.  Judgment under uncertainty: Conservatism in human information processing , 1982 .

[48]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[49]  Angela Ribeiro,et al.  Modelling Wild-Oat Density in Terms of Soil Factors: A Machine Learning Approach , 2005, Precision Agriculture.

[50]  Özer Çinar,et al.  Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity , 2006 .

[51]  D. Kumar,et al.  Irrigation planning using Genetic Algorithms , 2004 .

[52]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[53]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[54]  S. S. Madeiro,et al.  Intelligent Modeling of Sugar-Cane Maturation , 2006 .

[55]  E. W. Stevens,et al.  DEVELOPMENT AND VALIDATION OF A NEURAL NETWORK MODEL FOR SOIL WATER CONTENT PREDICTION WITH COMPARISON TO REGRESSION TECHNIQUES , 1999 .

[56]  YangQuan Chen,et al.  Fusion of soft computing and hard computing: computational structures and characteristic features , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[57]  Noel D.G. White,et al.  Classification of Fungal Infected Wheat Kernels Using Near-Infrared Reflectance Hyperspectral Imaging and Support Vector Machine , 2007 .

[58]  Jitendra Paliwal,et al.  Spectral Data Compression and Analyses Techniques to Discriminate Wheat Classes , 2006 .

[59]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[60]  Chun-Chieh Yang,et al.  Development of neural networks for weed recognition in corn fields , 2002 .

[61]  Y. Pachepsky,et al.  Stochastic imaging of soil parameters to assess variability and uncertainty of crop yield estimates , 1998 .

[62]  John J. Grefenstette,et al.  Using Genetic Algorithms for Concept Learning , 2004, Machine Learning.

[63]  Michio Sugeno,et al.  Applied fuzzy systems , 1994 .

[64]  Chun-Chieh Yang,et al.  APPLICATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) TO SIMULATE SOIL TEMPERATURE , 2004 .

[65]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[66]  Stephen A. Ritz,et al.  Distinctive features, categorical perception, and probability learning: some applications of a neural model , 1977 .

[67]  Marcel G. Schaap,et al.  Development of Pedotransfer Functions for Estimation of Soil Hydraulic Parameters using Support Vector Machines , 2009 .

[68]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[69]  S. Prasher,et al.  Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn , 2003 .

[70]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[71]  Stephen Grossberg,et al.  Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions , 1976, Biological Cybernetics.

[72]  D. Eddy.  Judgment under uncertainty: Probabilistic reasoning in clinical medicine: Problems and opportunities , 1982 .

[73]  Vincent Baeten,et al.  Screening of compound feeds using NIR hyperspectral data , 2006 .

[74]  Lei Tian,et al.  COLOR IMAGE SEGMENTATION WITHGENETIC ALGORITHM FOR IN-FIELD WEED SENSING , 2000 .

[75]  Petre I. Miu,et al.  Optimal Design and Process of Threshing Units Based on a Genetic Algorithm. II. Application , 2001 .

[76]  Ian Witten,et al.  Data Mining , 2000 .

[77]  M. Gupta,et al.  FUZZY INFORMATION AND DECISION PROCESSES , 1981 .

[78]  Tao Pei,et al.  Construction of membership functions for predictive soil mapping under fuzzy logic , 2010 .

[79]  T. Morimoto,et al.  DYNAMIC OPTIMIZATION USING NEURAL NETWORKS AND GENETIC ALGORITHMS FOR TOMATO COOL STORAGE TO MINIMIZE WATER LOSS , 2003 .

[80]  Karim C. Abbaspour,et al.  Estimation of surface shear strength in Zagros region of Iran - a comparison of artificial neural networks and multiple-linear regression models. , 2009 .

[81]  Lucien Duckstein,et al.  Artificial neural networks and multicriterion analysis for sustainable irrigation planning , 2006, Comput. Oper. Res..

[82]  Robert H. Wilkinson,et al.  A Method of Generating Functions of Several Variables Using Analog Diode Logic , 1963, IEEE Trans. Electron. Comput..

[83]  Jing Liu,et al.  Neural networks for setting target corn yields , 2000 .

[84]  J. A. Thomasson,et al.  Ground-Based Sensing System for Cotton Nitrogen Status Determination , 2006 .

[85]  George E. Meyer,et al.  Adaptive Image Segmentation using a Fuzzy Neural Network and Genetic Algorithm for Weed Detection , 2003 .

[86]  Ibrahim Onaran,et al.  Detection of Underdeveloped Hazelnuts from Fully Developed Nuts by Impact Acoustics , 2006 .

[87]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[88]  Wu Lanlan,et al.  Eggshell crack detection by acoustic impulse response and support vector machine. , 2009 .

[89]  Changqing Wu,et al.  Least Square Support Vector Machine Analysis for the Classification of Paddy Seeds by Harvest Year , 2008 .

[90]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[91]  A-Xing Zhu,et al.  A case-based reasoning approach to fuzzy soil mapping , 2002 .

[92]  W. S. Lee,et al.  Robotic Weed Control System for Tomatoes , 2004, Precision Agriculture.

[93]  R J Poppi,et al.  Application of artificial neural networks to the classification of soils from São Paulo State using near-infrared spectroscopy. , 2001, The Analyst.

[94]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[95]  GrossbergS..  Adaptive pattern classification and universal recoding , 1976 .

[96]  A. J. Clemmens,et al.  Bayesian Inference for Feedback Control. II: Surface Irrigation Example , 1992 .

[97]  Ashu Jain,et al.  Development of effective and efficient rainfall‐runoff models using integration of deterministic, real‐coded genetic algorithms and artificial neural network techniques , 2004 .

[98]  Kaoru Hirota,et al.  Industrial Applications of Fuzzy Technology , 1993, Springer Japan.

[99]  Markus Olhofer,et al.  Application reviews , 2005, Appl. Soft Comput..

[100]  Serge Guillaume,et al.  Influence of micrometeorological factors on pesticide loss to the air during vine spraying : Data analysis with statistical and fuzzy inference models , 2008 .

[101]  S. C. Negi,et al.  Modelling Total Suspended Solids in Vegetative Filter Strips using Artificial Neural Networks , 2003 .

[102]  S. Grossberg,et al.  Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors , 1976, Biological Cybernetics.

[103]  Chun-Chieh Yang,et al.  APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR SIMULATION OF SOIL TEMPERATURE , 1997 .

[104]  Yafit Cohen,et al.  Developing Spatial Decision Support System for Medfly Control in Israel , 2006 .

[105]  Chun-Chieh Yang,et al.  Recognition of weeds with image processing and their use with fuzzy logic for precision farming , 2000 .

[106]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[107]  S. O. Prasher,et al.  Application of artificial neural networks in image recognition and classification of crop and weeds , 2000 .

[108]  Lei Tian,et al.  NARROW-BAND AND DERIVATIVE-BASED VEGETATION INDICES FOR HYPERSPECTRAL DATA , 2004 .

[109]  K. Srinivasa Raju,et al.  Optimal Reservoir Operation for Irrigation of Multiple Crops Using Genetic Algorithms , 2006 .

[110]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[111]  Scott A. Shearer,et al.  BACKPROPAGATION NEURAL NETWORK DESIGN AND EVALUATION FOR CLASSIFYING WEED SPECIES USING COLOR IMAGE TEXTURE , 2000 .

[112]  E. M. Barnes,et al.  Fuzzy composite programming to combine remote sensing and crop models for decision support in precision crop management , 2000 .

[113]  László T. Kóczy,et al.  A survey on universal approximation and its limits in soft computing techniques , 2003, Int. J. Approx. Reason..

[114]  S. Kim,et al.  NEURAL NETWORK MODELING AND FUZZY CONTROL SIMULATION FOR BREAD-BAKING PROCESS , 1997 .

[115]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[116]  Eakasit Sritham,et al.  Detecting Eggshell Cracks by Acoustic Impulse Response and Artificial Neural Networks , 2003 .

[117]  Chun-Chieh Yang,et al.  DIFFERENTIATION OF CROP AND WEEDS BY DECISION-TREE ANALYSIS OF MULTI-SPECTRAL DATA , 2004 .

[118]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[119]  Seppo J. Ovaska,et al.  Fusion of soft and hard computing: multi-dimensional categorization of computationally intelligent hybrid systems , 2007, Neural Computing and Applications.

[120]  T. Bayes.  An essay towards solving a problem in the doctrine of chances , 2003 .

[121]  Pradeep K. Goel,et al.  Modeling Sediment and Phosphorous Movement through Vegetative Filter Strips using Artificial Neural Networks and GRAPH , 2004 .

[122]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[123]  R. T. Walczak,et al.  Using Support Vector Machines to Develop Pedotransfer Functions for Water Retention of Soils in Poland , 2008 .

[124]  Q. Zhang,et al.  A HYBRID APPROACH TO HYDRAULIC VANE PUMP CONDITION MONITORING AND FAULT DETECTION , 2006 .

[125]  S. Kuo,et al.  Decision support for irrigation project planning using a genetic algorithm , 2000 .

[126]  G. Qu,et al.  MEASURING ODOR CONCENTRATION WITH AN ELECTRONIC NOSE , 2000 .

[127]  Seppo J. Ovaska,et al.  Fusion of soft computing and hard computing in industrial applications: an overview , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[128]  J. J. Stoorvogel,et al.  A functional approach to soil characterization in support of precision agriculture , 2000 .

[129]  Luis A. Garcia,et al.  Sensitivity of Irrigation Water Balance to Climate Change in the Great Plains of Colorado , 2006 .

[130]  Lav R. Khot,et al.  Neural-Network-Based Classification of Meat: Evaluation of Techniques to Overcome Small Dataset Problems , 2008 .

[131]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[132]  Isao Hayashi,et al.  NN-driven fuzzy reasoning , 1991, Int. J. Approx. Reason..

[133]  Long Qi,et al.  Rice Blast Detection Using Multispectral Imaging Sensor and Support Vector Machine , 2009 .

[134]  Ravi Jain,et al.  D-SCIDS: Distributed soft computing intrusion detection system , 2007, J. Netw. Comput. Appl..

[135]  S. Liang,et al.  Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model , 2003 .

[136]  Kenneth A. Sudduth,et al.  STATISTICAL AND NEURAL METHODS FOR SITE–SPECIFIC YIELD PREDICTION , 2003 .

[137]  Lei Tian,et al.  Artificial Intelligence Controller for Automatic Multispectral Camera Parameter Adjustment , 2007 .

[138]  Kuang-Wen Hsieh,et al.  A DYNAMIC SIMULATION MODEL FOR SEEDLING GROWTH , 2001 .

[139]  Lei Tian,et al.  AERIAL CIR REMOTE SENSING FOR WEED DENSITY MAPPING IN A SOYBEAN FIELD , 2001 .

[140]  Reza Langari,et al.  Supervisory fuzzy adaptive control of a binary distillation column , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[141]  Chun-Chieh Yang,et al.  AN ARTIFICIAL NEURAL NETWORK MODEL FOR SIMULATING PESTICIDE CONCENTRATIONS IN SOIL , 1997 .

[142]  H. Ramon,et al.  A Weed Species Spectral Detector Based on Neural Networks , 2002, Precision Agriculture.

[143]  Ian J. Yule,et al.  Economic and Environmental Opportunities from Utilizing VRAT from Aircraft for Improved Placement of Fertilizer , 2005 .

[144]  Louis D. Albright,et al.  PREDICTIVE NEURAL NETWORK MODELING OF pH AND ELECTRICAL CONDUCTIVITY IN DEEP–TROUGH HYDROPONICS , 2002 .

[145]  Lei Tian,et al.  Environmentally adaptive segmentation algorithm for outdoor image segmentation , 1998 .

[146]  Bart De Ketelaere,et al.  A neural network based plant classifier , 2001 .

[147]  Sankar K. Pal,et al.  Logical operation based fuzzy MLP for classification and rule generation , 1994, Neural Networks.

[148]  Walter D. Potter,et al.  A genetic algorithm for aerial spray application optimization. , 2000 .

[149]  Jonathan L. Shapiro,et al.  Genetic Algorithms in Machine Learning , 2001, Machine Learning and Its Applications.

[150]  Joachim Ingwersen,et al.  Estimation of heavy metal sorption in German soils using artificial neural networks , 2009 .

[151]  P. Geladi,et al.  Selection of near Infrared Wavelengths Using Genetic Algorithms for the Determination of Seed Moisture Content , 2003 .

[152]  Mark Chignell,et al.  A framework for artificial intelligence applications software development , 1985 .

[153]  Steffen L. Lauritzen,et al.  Some modern applications of graphical models , 2003 .

[154]  P. Krishnan,et al.  Nozzle wear rate prediction using regression and neural network , 2002 .

[155]  J. W. Mishoe,et al.  Parameter Adjustment to a Crop Model Using a Sensor-based Decision Support System , 1993 .

[156]  S. I. Cho,et al.  AUTONOMOUS SPEED SPRAYER GUIDANCE USING MACHINE VISION AND FUZZY LOGIC , 1999 .

[157]  Thomas S. Colvin,et al.  A Fuzzy Logic Yield Simulator For Prescription Farming , 1994 .

[158]  Yibin Ying,et al.  Quantitative analysis of fruit firmness by near infrared spectroscopy and least-squares support vector machine , 2009 .

[159]  Calvin D. Perry,et al.  COTTON YIELD RESPONSE TO VARIABLE RATE NEMATICIDES ACCORDING TO RISK ZONES , 2008 .

[160]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[161]  A-Xing Zhu,et al.  Fuzzy soil mapping based on prototype category theory , 2006 .

[162]  Zoubin Ghahramani,et al.  Learning Dynamic Bayesian Networks , 1997, Summer School on Neural Networks.

[163]  R. W. McClendon,et al.  Estimating daily pan evaporation with artificial neural networks , 2000 .

[164]  Lei Tian,et al.  CLASSIFICATION OF BROADLEAF AND GRASS WEEDS USING GABOR WAVELETS AND AN ARTIFICIAL NEURAL NETWORK , 2003 .

[165]  Elizabeth C. Hirschman,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[166]  S. Prasher,et al.  Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data , 2005 .

[167]  Yibin Ying,et al.  Support Vector Machines and Near Infrared Spectroscopy for Quantification of Vitamin C Content in Kiwifruit , 2008 .

[168]  Daniel C. Yoder,et al.  Estimation of reference crop evapotranspiration using fuzzy state models , 2001 .

[169]  Pekka Linko,et al.  Fuzzy logic and neural network applications in food science and technology , 1993 .

[170]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[171]  Peter Bajcsy,et al.  Hyperspectral image data mining for band selection in agricultural applications , 2004 .

[172]  Y. Suzuki,et al.  Development of discriminant model for weed detection using hyperspectral imaging. , 2009 .

[173]  H. V. Jagadish,et al.  Database Modeling and Design , 1998 .

[174]  Naiqian Zhang,et al.  Comparing Adaptive Neuro-Fuzzy Inference System (ANFIS) to Partial Least-Squares (PLS) method for Simultaneous Prediction of Multiple Soil Properties , 2003 .

[175]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[176]  Won Suk Lee,et al.  Citrus Yield Mapping System on a Canopy Shake and Catch Harvester , 2007 .

[177]  Xiuqin Rao,et al.  Gaussian Mixture Model-Based Walnut Shell and Meat Classification in Hyperspectral Fluorescence Imagery , 2007 .

[178]  Siza D. Tumbo,et al.  HYPERSPECTRAL–BASED NEURAL NETWORK FOR PREDICTING CHLOROPHYLL STATUS IN CORN , 2002 .

[179]  A-Xing Zhu,et al.  Soil Mapping Using GIS, Expert Knowledge, and Fuzzy Logic , 2001 .

[180]  R. W. McClendon,et al.  DETERMINATION OF CULTIVAR COEFFICIENTS OF CROP MODELS USING A GENETIC ALGORITHM: A CONCEPTUAL FRAMEWORK , 1999 .

[181]  Petre I. Miu.  Optimal Design and Process of Threshing Units Based on a Genetic Algorithm. I. Algorithm , 2001 .

[182]  John A. Marchant,et al.  Comparison of a Bayesian classifier with a multilayer feed-forward neural network using the example of plant/weed/soil discrimination , 2003 .

[183]  Yanbo Huang,et al.  Advances in Artificial Neural Networks - Methodological Development and Application , 2009, Algorithms.

[184]  P. Dutilleul,et al.  CLASSIFICATION ACCURACY OF DISCRIMINANT ANALYSIS, ARTIFICIAL NEURAL NETWORKS, AND DECISION TREES FOR WEED AND NITROGEN STRESS DETECTION IN CORN , 2005 .

[185]  Robert S. Freeland,et al.  Subsurface Characterization using Textural Features Extracted from GPR Data , 2007 .

[186]  Chun-Chieh Yang,et al.  Development of an Image Processing System and a Fuzzy Algorithm for Site-Specific Herbicide Applications , 2003, Precision Agriculture.

[187]  Yong He,et al.  Variety Identification of Chinese Cabbage Seeds Using Visible and Near-Infrared Spectroscopy , 2008 .

[188]  S. K. Upadhyaya,et al.  PREDICTION OF PROCESSING TOMATO YIELD USING A CROP GROWTH MODEL AND REMOTELY SENSED AERIAL IMAGES , 2005 .

[189]  J.M. Giron-Sierra,et al.  Genetic algorithms for fuzzy control of automatic docking with a space station , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[190]  Amin Elshorbagy,et al.  Estimating Saturated Hydraulic Conductivity Using Genetic Programming , 2007 .

[191]  M. Trebar,et al.  Application of distributed SVM architectures in classifying forest data cover types , 2008 .

[192]  G. Bragato.  Fuzzy continuous classification and spatial interpolation in conventional soil survey for soil mapping of the lower Piave plain , 2004 .

[193]  Yoshiki Uchikawa,et al.  On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm , 1992, IEEE Trans. Neural Networks.

[194]  James L. McClelland,et al.  James L. McClelland, David Rumelhart and the PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition . Vol. 1. Foundations . Vol. 2. Psychological and biological models . Cambridge MA: M.I.T. Press, 1987. , 1989, Journal of Child Language.

[195]  Seppo J. Ovaska.  Computationally Intelligent Hybrid Systems , 2004 .

[196]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[197]  Kazuo Tanaka,et al.  Application of practical fuzzy-PID hybrid control system to petrochemical plant , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[198]  R. Bruce Curry,et al.  Agricultural Systems modeting and Simulation , 1997 .

[199]  Won Suk Lee,et al.  STATISTICAL AND NEURAL NETWORK CLASSIFIERS FOR CITRUS DISEASE DETECTION USING MACHINE VISION , 2005 .

[200]  Tetsuo Morimoto,et al.  Optimization of a fuzzy controller for fruit storage using neural networks and genetic algorithms , 1997 .

[201]  Woodie C. Flowers,et al.  A genetic algorithm for resource-constrained scheduling , 1996 .

[202]  J. Nie,et al.  Neural network-based approximate reasoning: principles and implementation , 1992 .

[203]  Bimal K. Bose,et al.  Fuzzy logic control of AC induction motors , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[204]  Martha C. Anderson,et al.  A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery ☆ , 2004 .

[205]  Suranjan Panigrahi,et al.  Self-organizing map combined with a fuzzy clustering for color image segmentation of edible beans , 2003 .

[206]  Yong He,et al.  Application of Infrared Spectroscopy Technique and Chemometrics for Measurement of Components in Rice after Radiation , 2009 .

[207]  S. K. Upadhyaya,et al.  Relationship Between Modified Normalized Difference Vegetation Index and Leaf Area Index for Processing Tomatoes , 2005 .

[208]  Jin-Young Jeong,et al.  AE—Automation and Emerging Technologies: Weed–plant Discrimination by Machine Vision and Artificial Neural Network , 2002 .

[209]  Yuxin Miao,et al.  Identifying important factors influencing corn yield and grain quality variability using artificial neural networks , 2006, Precision Agriculture.

[210]  Seong G. Kong,et al.  Principal component analysis for poultry tumor inspection using hyperspectral fluorescence imaging , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[211]  George E. Meyer,et al.  A crop water stress index for tall fescue (Festuca arundinacea Schreb.) irrigation decision-making — a traditional method , 2001 .

[212]  Dallas E. Peterson,et al.  WEED DETECTION USING COLOR MACHINE VISION , 2000 .

[213]  Lei Tian,et al.  A genetic-algorithm-based selective principal component analysis (GA-SPCA) method for high-dimensional data feature extraction , 2003, IEEE Trans. Geosci. Remote. Sens..

[214]  Chun-Chieh Yang,et al.  APPLICATION OF DATA MINING TECHNOLOGY FOR HYPERSPECTRAL IMAGERY CLASSIFICATION IN AGRICULTURAL FIELDS , 2001 .

[215]  Yibin Ying,et al.  Comparison of chemometrics methods for assessing internal quality of pears on-line using visible/near infrared transmission technique , 2008 .

[216]  Frederick S. Hillier,et al.  Introduction of Operations Research , 1967 .

[217]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[218]  R. M. Lark,et al.  Designing sampling grids from imprecise information on soil variability, an approach based on the fuzzy kriging variance , 2000 .

[219]  Siza D. Tumbo,et al.  ON–THE–GO SENSING OF CHLOROPHYLL STATUS IN CORN , 2002 .

[220]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.