Improving the crop yield is always a major challenge for farming community as well for agricultural scientists. Though various computational approaches have been followed traditionally in practice, still a persistent decision making method to improve crop yield is not predicted and lies as a misnomer. Multiple rethegorical factors / parameters form the basis of improved crop yield. This paper proposes Fuzzy Cognitive Map approach to help in decisive making and suggesting an optimized solution of improving crop yield as well as compared with existing approaches such as Genetic Algorithm (GA) and Artificial Neural Network (ANN). The research work considers two major crops namely Sugarcane and Red Chilli for analysis. The results show that FCM provides a higher accuracy in supporting crop growth yield with higher similarity ratio of expected growth and actual growth. © 2015, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.