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RD b Y a b c d a A R R A K S F A G C P C 0 dComputers and Electronics in Agriculture 71 (2010) 107–127 Contents lists available at ScienceDirect Computers and Electronics in Agriculture journa l homepage: www.e lsev ier .com/ locate /compag eview evelopment of soft computing and applications in agricultural and iological engineering anbo Huanga,?, Yubin Lanb, Steven J. Thomsona, Alex Fangc, Wesley C. Hoffmannb, Ronald E. Laceyd USDA-ARS, CPSRU, 141 Experiment Station Road, Stoneville, MS 38776, United States USDA-ARS, APMRU, 2771 FB Road, College Station, TX 77845, United States Engineering Technology and Industrial Distribution, Texas AM University, College Station, TX 77843, United States Biological and Agricultural Engineering, Texas AM University, College Station, TX 77843, United States r t i c l e i n f o rticle history: eceived 1 August 2009 eceived in revised form 9 January 2010 ccepted 25 January 2010 a b s t r a c t Soft computing is a set of “inexact” computing techniques,which are able tomodel and analyze very com- plex 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 biologicaleywords: oft computing uzzy logic rtificial neural networks enetic algorithms rop management engineering, researchers and engineers have developedmethods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vectormachines to study soil andwater regimes related to crop growth, analyze the operation of foodprocessing, and support decision-making in precision farming. This paper reviews thedevelopmentof soft computing techniques.With the concepts andmethods, appli- cations of soft computing in thefield of agricultural andbiological engineering are presented, especially in the soil andwater context for