Land suitability studies include the comparison of land characteristics and climatic conditions with land utilization type requirements in agricultural lands and the selection of the best land utilization type for cropping. In such investigations, climatic factors and soil physical and chemical characteristics are studied .The objective of this study were to evaluate the efficiency of fuzzy logic to predict the yield of important crops and to compare the fuzzy logic and Boolean logic based land evaluation methods in the study area covering 6832 ha between the two cities of Zarrinshahr and Mobarakeh. This investigation involves several studies including a soil survey study, a lab study, agronomic studies and, finally, qualitative and quantitative land suitability evaluation with fuzzy and Boolean logic for wheat and rice. A soil map with 4 soil series and 21 soil series phases was prepared. Factors affecting the wheat and rice production consist of climatic conditions and edaphologic properties such as topography, water table, EC, pH, ESP, percent of clay, silt, sand, gravel, gypsum and CaCO 3 content. Climatic data collected at the Isfahan synoptic station were used. Qualitative land suitability evaluation was carried out using the Boolean approach and three methods including simple limitation, intensity of limitations and parametric. Also, crops potential yield was determined using climatic parameters and then quantitative land suitability evaluation was performed. Using Matlab software, qualitative and quantitative land evaluation were done based on fuzzy logic approach. In fuzzy approach, we first normalized land characteristics data for calculating the weights. Land characteristic weight matrix was made by perceptron neural network approach. Membership degrees matrix was also prepared. To determine the membership degrees, we used Gaussian, s-shape and z-shape membership functions. Parameters of function shapes were transformed to equations with variable coefficients and the best coefficients were eventually chosen based on the model determination coefficient. Weight matrix was then multiplied by Membership degrees matrix to calculate the soil index values. We integrated soil index and climate degree with root square equation to reach the land index. Evaluation of fuzzy and Boolean methods was carried out by comparing the regression determination coefficient between land and yield coefficients. The determination coefficient in Boolean approach for wheat and rice was 0.63 and 0.70, respectively In fuzzy approach, it was 0.83 and 0.86 for wheat and rice, respectively. In conclusion, the fuzzy approach seems to have a higher efficiency than Boolean method for land evaluation. In addition, our results show that based on both fuzzy and Boolean approaches and due to moisture