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Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

机译：使用自组织图确定土壤肥力（案例研究：设拉子平原）

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Soil fertility refers to the ability of a soil to supply plant nutrients. Naturally, micro and macro elements are made available to plants by breakdown of the mineral and organic materials in the soil. Artificial neural network (ANN) provides deeper understanding of human cognitive capabilities. Among various methods of ANN and learning an algorithm, self-organizing maps (SOM) are one of the most popular neural network models. The aim of this study was to classify the factors influencing soil fertility in Shiraz plain, southern Iran. The relationships among soil features were studied using the SOM in which, according to qualitative data, the clustering tendency of soil fertility was investigated using seven parameters (N, P, K, Fe, Zn, Mn, and Cu). The results showed that for soil fertility there is a close relationship between P and N, and also between P and Zn. The other parameters, such as K, Fe, Mn, and Cu, are not mutually related. The results showed that there are six clusters for soil fertility and also that group 1 soils are more fertile than the other.
机译：土壤肥力是指土壤提供植物养分的能力。自然地，通过分解土壤中的矿物质和有机物质，可将微量元素和宏观元素提供给植物。人工神经网络（ANN）提供了对人类认知能力的更深刻理解。在ANN的各种方法和学习算法中，自组织映射（SOM）是最流行的神经网络模型之一。这项研究的目的是对影响伊朗南部设拉子平原土壤肥力的因素进行分类。利用SOM研究了土壤特征之间的关系，根据定性数据，使用七个参数（N，P，K，Fe，Zn，Mn和Cu）研究了土壤肥力的聚集趋势。结果表明，对于土壤肥力，磷和氮之间以及磷和锌之间都有密切的关系。其他参数（例如K，Fe，Mn和Cu）互不相关。结果表明，土壤肥力有6个簇，第1组土壤比其他土壤更肥沃。

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