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.

中文翻译：

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戈勒斯坦省基于土壤性质估算藏红花产量的不同方法比较

摘要 土壤特性与藏红花产量估算之间关系的评估可能有助于农业规划寻找适合这种有价值产品生长的土地。本研究旨在研究人工神经网络 (ANN)、多元线性回归 (MLR) 和自适应神经模糊推理系统 (ANFIS) 在伊朗 Golestan 省部分土地的藏红花产量估计方面的性能。为此，选择了 100 个藏红花种植区。为了快速和低成本地估算藏红花产量，使用 MLR、ANN 和 ANFIS 方法基于土壤特性作为输入设计了六种不同的模型。根据结果​​，与 MLR (R2 = 0.41–0.47) 和 ANFIS (R2 = 0.41–0.69) 模型相比，ANN 在估计藏红花产量方面显示出最高的准确度 (R2 = 0.58–0.89)。从这三种方法中定义的六个模型获得的结果的比较表明，模型 4（R2 Reg = 0.45，R2 ANFIS = 0.57，R2 ANN = 0.87），输入有机磷、钾和碳酸钙，是估计藏红花产量磷的准确性和速度方面的最佳模型。模型中人工神经网络的 RI 指数相对于 MLR 和 ANFIS 分别为 50% 和 34%，表明人工神经网络在藏红花产量估计中的准确性更高。研究结果可用于利用土壤中的有机磷和有机质水平来确定研究区适合种植藏红花的土地。是估计藏红花产量磷的准确性和速度方面的最佳模型。模型中人工神经网络的 RI 指数相对于 MLR 和 ANFIS 分别为 50% 和 34%，表明人工神经网络在藏红花产量估计中的准确性更高。研究结果可用于利用土壤中的有机磷和有机质水平来确定研究区适合种植藏红花的土地。是估计藏红花产量磷的准确性和速度方面的最佳模型。模型中人工神经网络的 RI 指数相对于 MLR 和 ANFIS 分别为 50% 和 34%，表明人工神经网络在藏红花产量估计中的准确性更高。研究结果可用于利用土壤中的有机磷和有机质水平来确定研究区适合种植藏红花的土地。