Comparison of Different Methods of Estimating Saffron Yield Based on Soil Properties in Golestan Province

Abstract

ABSTRACT Evaluation of the relationship between soil properties and saffron yield estimation may contribute to agricultural planning in finding suitable lands for the growth of this valuable product. This study aimed to investigate the performance of artificial neural network (ANN), multiple linear regression (MLR), and adaptive neuro-fuzzy inference system (ANFIS) in terms of saffron yield estimation in some lands of Golestan province, Iran. To this end, 100 areas under saffron cultivation were selected. For rapid and low-cost saffron yield estimation, six different models were designed based on soil properties as inputs using MLR, ANN, and ANFIS methods. According to the results, ANN showed the highest accuracy (R2 = 0.58–0.89) in estimating saffron yield as compared to MLR (R2 = 0.41–0.47) and ANFIS (R2 = 0.41–0.69) models. A comparison of the results obtained from the six models defined in these three methods indicated that Model 4 (R2 Reg = 0.45, R2 ANFIS = 0.57, R2 ANN = 0.87), with the inputs, organic phosphorus, potassium, and calcium carbonate, was the best model in terms of accuracy and speed in estimating saffron yield phosphorus. The RI indexes for ANN in the model were 50% and 34% relative to MLR and ANFIS, respectively, demonstrating the higher accuracy of ANN in saffron yield estimation. The study results can be used to identify lands suitable for saffron cultivation in the study area using organic phosphorus and organic matter levels in the soil.