Development of soft computing and applications in agricultural and biological engineering

被引:203
作者
Huang, Yanbo [1 ]
Lan, Yubin [2 ]
Thomson, Steven J. [1 ]
Fang, Alex [3 ]
Hoffmann, Wesley C. [2 ]
Lacey, Ronald E. [3 ]
机构
[1] ARS, USDA, CPSRU, Stoneville, MS 38776 USA
[2] ARS, USDA, APMRU, College Stn, TX 77845 USA
[3] Texas A&M Univ, College Stn, TX 77843 USA
关键词
Soft computing; Fuzzy logic; Artificial neural networks; Genetic algorithms; Crop management; Precision agriculture; ARTIFICIAL NEURAL-NETWORKS; LEAF-AREA INDEX; NITROGEN STRESS DETECTION; COLOR IMAGE SEGMENTATION; SUPPORT VECTOR MACHINES; FUZZY-LOGIC; GENETIC-ALGORITHM; PEDOTRANSFER FUNCTIONS; DECISION-SUPPORT; PARAMETER ADJUSTMENT;
D O I
10.1016/j.compag.2010.01.001
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
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. Published by Elsevier B.V.
引用
收藏
页码:107 / 127
页数:21
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