ABSTRACT

The aim of the present research is to estimate the saffron yield by land characteristics through a combination of adaptive neural-fuzzy inference system and particle swarm optimization in Siminehrood catchment, south of Urmia Lake, Iran. To achieve this target, 150 representative soil profiles were descripted in saffron fields. Then each genetic horizon was sampled for soil analysis. Climate rating was calculated from meteorological data by Food and Agriculture Organization framework. Saffron observed yield obtained from saffron field data. The results showed that coarse fragment, gypsum, electrical conductivity, organic carbon, cation exchange capacity, pH, calcium carbonate equivalent and climate rating have the highest correlation with saffron yield. The range of saffron estimated yield was between 1937–4124 and 1843–4025 kg per hectare for combination model and fuzzy model, respectively. Combination model doses a more accurate estimation compared with the observed yield values (2020–4200), statistical validation indicator results confirm this too. High agreement obtained from combination model between estimated saffron yield map with observed yield map. Finally, a combination of adaptive neural-fuzzy inference system and particle swarm optimization model can be employed as a powerful, low time-consuming and accurate method for estimating saffron yield in pre-decision for saffron cultivation.

摘要

本研究的目的是通过结合自适应神经模糊推理系统和粒子群优化，在伊朗 Urmia 湖以南的 Siminehrood 流域，根据土地特征估计藏红花产量。为实现这一目标，在藏红花田中描述了 150 个具有代表性的土壤剖面。然后对每个遗传层进行采样以进行土壤分析。气候评级是根据粮食及农业组织框架的气象数据计算得出的。藏红花观察到从藏红花田数据中获得的产量。结果表明，粗碎片、石膏、电导率、有机碳、阳离子交换容量、pH、碳酸钙当量和气候等级与藏红花产量的相关性最高。组合模型和模糊模型的藏红花估计产量范围分别在 1937-4124 和 1843-4025 千克/公顷之间。与观察到的产量值（2020-4200）相比，组合模型给出了更准确的估计，统计验证指标结果也证实了这一点。从估计的藏红花产量图与观察到的产量图之间的组合模型获得的高度一致性。最后，自适应神经模糊推理系统和粒子群优化模型的组合可以作为一种强大、耗时低且准确的藏红花产量预估方法用于藏红花栽培的预决策。统计验证指标结果也证实了这一点。从估计的藏红花产量图与观察到的产量图之间的组合模型获得的高度一致性。最后，自适应神经模糊推理系统和粒子群优化模型的组合可以作为一种强大、耗时低且准确的藏红花产量预估方法用于藏红花栽培的预决策。统计验证指标结果也证实了这一点。从估计的藏红花产量图与观察到的产量图之间的组合模型获得的高度一致性。最后，自适应神经模糊推理系统和粒子群优化模型的组合可以作为一种强大、耗时低且准确的藏红花产量预估方法用于藏红花栽培的预决策。