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A Review of Soft Computing Based DSS in Fertilizer Management in India


Affiliations
1 YMT College of Management, Kharghar-Navi Mumbai, India
 

Soft computing is a problem solving methodology applied in the domain of impreciseness to address complex analytical problems and to model solution framework based on human intelligence. Soft computing paradigm is imminent as a decision support model in the domain of uncertainty, vagueness and human behavior. The methodology of soft computing is studied and applied in many areas of research and development in last few years. In agricultural sector researchers and analysts have developed new methods using soft computing constituents like evolutionary computing, fuzzy logic, artificial neural network and genetic algorithm to study soil conditions, to analyze fertilizer applications and to monitor crop growth in view of improving crop productivity. Fertilizer management is a multifaceted and complex process that greatly affects crop productivity. This paper presents the study of soft computing techniques in agricultural domain. A review of Soft computing methodology to improve crop productivity, to perform complex analysis and to support decision making with respect to fertilizer management is presented.

Keywords

Soft Computing, DSS, Agriculture, Fertilizer Management.
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  • A Review of Soft Computing Based DSS in Fertilizer Management in India

Abstract Views: 513  |  PDF Views: 211

Authors

Sharayu G. Karandikar
YMT College of Management, Kharghar-Navi Mumbai, India

Abstract


Soft computing is a problem solving methodology applied in the domain of impreciseness to address complex analytical problems and to model solution framework based on human intelligence. Soft computing paradigm is imminent as a decision support model in the domain of uncertainty, vagueness and human behavior. The methodology of soft computing is studied and applied in many areas of research and development in last few years. In agricultural sector researchers and analysts have developed new methods using soft computing constituents like evolutionary computing, fuzzy logic, artificial neural network and genetic algorithm to study soil conditions, to analyze fertilizer applications and to monitor crop growth in view of improving crop productivity. Fertilizer management is a multifaceted and complex process that greatly affects crop productivity. This paper presents the study of soft computing techniques in agricultural domain. A review of Soft computing methodology to improve crop productivity, to perform complex analysis and to support decision making with respect to fertilizer management is presented.

Keywords


Soft Computing, DSS, Agriculture, Fertilizer Management.