Pesticide Application Technologies for Spray-drift Management, Maximizing In-field Deposition, and Targeted Spraying
Location: Crop Production Systems Research Unit
Title: Technical Development and Application of Soft Computing in Agricultural and Biological Engineering
Submitted to: ASABE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: May 6, 2009
Publication Date: June 23, 2009
Citation: Huang, Y., Lan, Y., Thomson, S.J., Fang, A., Hoffmann, W.C., Lacey, R. 2009. Technical Development and Application of Soft Computing in Agricultural and Biological Engineering. ASABE Annual International Meeting, June 21-24,2009, Reno, NV. p.095972.
Interpretive Summary: Soft computing is a computing technique that is related to but also differs in many ways from hard computing. Hard computing contains a large set of conventional computing techniques such as stochastic and statistical methods. These methods are used to solve problems that have a direct connection between the size of a problem and the amount of resources needed to solve the problem. However, some problems may be so large that, even at super computing speeds, it still would take a lifetime to solve them. Soft computing was proposed to overcome the problems of hard computing by using inexact intelligent methods to give useful but inexact answers to very complex problems. Soft computing mimics human intelligence. A number of important techniques are included in soft computing: fuzzy logic, which models human reasoning in imprecise environments (not simply ‘yes or no’); artificial neural networks, which model interconnecting neurons from human brain studies; and genetic algorithms, which is a problem solving scheme that mimics the processes of evolutionary biology. Soft computing has been widely used in engineering computing. In agricultural and biological engineering, fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines (the latter being a new soft computing technique), have been developed for studying soil and water problems related to crop growth management, design, analysis and control of food processing, and decision support in precision farming. Clarification of the limitations of soft computing will allow scientists to project changes and emerging issues in the field over the coming decades. This will assist in the design of new research and applications, especially in agricultural and biological engineering. This paper discusses soft computing development and applications in agricultural and biological engineering within the soil and water context for crop management and decision support for precision agriculture.
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 will review the development of soft computing techniques, and a number of advanced soft computing techniques will be introduced. With these concepts and methods, applications of soft computing in the field of agricultural and biological engineering will be presented, especially in the soil and water context for crop management and decision support for precision agriculture. The future of development and application of soft computing in agricultural and biological engineering will be discussed.