## 1 Introduction  
Selenium (Se) is one of the essential trace elements for the human body [1, 2]. It exerts biological functions such as antioxidant, anti-inflammation, anticancer activity, immune enhancement, and anti-aging, primarily through its role as a cofactor in selenoproteins, particularly glutathione peroxidase, which protects cells from oxidative damage by detoxifying lipid peroxides [3, 4, 5]. Prolonged Se deficiency in humans can lead to well-known endemic diseases, such as Keshan disease and Kashin-Beck disease, by impairing cardiovascular function and increasing the risk of myocardial infarction [6, 7]. China is a country with soils severely deficient in Se, with an uneven distribution of Se resources, resulting in varying degrees of Se deficiency across most regions [8, 9]. The primary source of Se for the human body is the consumption of Se-rich products [10, 11, 12]. Thus, Se-rich foods are not only essential for meeting daily Se intake but are also increasingly in demand due to heightened health awareness, leading to expanding market opportunities and rising economic value [9; 13]. Located in Xiangyang City, Hubei Province, Nanzhang County is one of the three core towns of Xiangyang's \"Organic Valley\" [14]. Situated in the transitional zone between the Jing Mountains and the Jianghan Plain, Nanzhang County enjoys natural ecological resources nurtured by a northern subtropical monsoon climate [15], providing unique conditions for the development of organic Se-rich agricultural products. Delineating optimized planting zones for Se-rich crops in Nanzhang County and employing precision agricultural practices to improve the quality and yield of local Se-rich products will enhance their market competitiveness and promote the development of regional specialty agriculture. Moreover, scientifically planning Se-rich crop planting zones can ensure the sustainable utilization of soil resources, fostering the long-term growth of the Se-rich product industry [16; 17]. Concurrently, by identifying areas suitable for Se-low crop cultivation, measures such as soil improvement, crop variety selection, precision fertilization, and irrigation can be implemented to effectively develop Se-deficient soils, enhance Se content in crops, and significantly alleviate Se deficiency in certain regions, potentially driving the growth of the Se-rich product industry [18; 19]. In previous studies, multivariate linear regression (MLR) models have been mainly used to predict the Se crop content [20; 21]. Geochemical data reveal that the relationship between Se crop content and soil properties is characterized by nonlinearity and emergent behaviors, where complex interactions between various factors give rise to outcomes that cannot be predicted simply by analyzing the individual components [22], limiting the applicability of traditional linear regression methods in this field. In recent years, the rapid development of fuzzy weights-of-evidence (FWofE) models and machine learning (ML) techniques has provided effective solutions to address these challenges. The FWofE model can quantitatively analyze the spatial relationships between the distribution of Se-rich crops and environmental influencing factors, enabling accurate predictions of Se-rich crop distribution. Comparative studies of ML approaches and multiple regression methods have demonstrated that ML techniques achieve higher accuracy and applicability in predicting Se crop content [21; 23]. Unlike the MLR method, ML methods such as backpropagation neural network (BP-NN) and support vector regression (SVR) can capture nonlinear relationships, emergent behaviors, and high-dimensional interactions among variables [24; 25]. In summary, compared to the MLR approach, FWofE, BP-NN, and SVR are particularly advantageous for modeling the intricate and nonlinear relationships between Se crop content and soil physicochemical properties, thereby supporting more precise predictions.