## 1 Introduction  
Zinc (Zn) is an essential trace element for the human body, playing a crucial role in physiological processes such as growth, reproduction, immunity, and endocrine functions [1]. Zinc deficiency can lead to weakened immunity and impaired digestive function, and, in children, it may cause stunted growth or, in severe cases, dwarfism. Studies have shown that approximately 40% of the global population faces health issues related to zinc deficiency [2]. Most people obtain zinc primarily from food, and insufficient zinc intake is considered one of the major causes of zinc deficiency [3]. Rice, a staple food for over 3.5 billion people worldwide, is closely linked to dietary zinc intake, especially in Asia [4]. However, in many regions, the zinc content in rice is very low [5,6], insufficient to meet human nutritional needs [7]. Therefore, the development of natural zinc-enriched rice from zinc-rich soils is of great importance for addressing the widespread issue of zinc deficiency. Rice's absorption of trace elements is influenced not only by the total amount of these elements in the soil but also by factors such as their bioavailability, the forms in which the elements occur in the soil, soil physicochemical properties, and the interactions between elements [8]. Previous research has shown that a high soil zinc content does not necessarily correlate with a high zinc content in rice grains. In most cases, there is no significant correlation between the two [9], making it difficult to classify zinc-rich soils based solely on the soil zinc content. Therefore, understanding the characteristics of rice's zinc absorption and constructing predictive models for the zinc content in rice grains is crucial for the development of zinc-rich land resources. In recent years, several studies have established models for predicting the absorption of heavy metals by rice [10,11], and machine learning methods have been widely used in the construction of these models, achieving good predictive results. Artificial neural networks (ANNs) and random forest (RF) are two of the most commonly applied machine learning methods and have shown excellent performance in predicting the heavy metal content in crops [12,13]. However, due to the high costs of field data collection and the intensive labor, equipment, and financial support required, as well as the complexity and variability of the soil environments in agricultural regions [14,15], predicting the heavy metal content in soil-rice systems using machine learning remains challenging in different research areas [9]. [PERSON] et al. [9] selected soil pH, TOC (total organic carbon), Mn, and Zn as the input variables and used an artificial neural network model to predict the bioaccumulation factor of zinc (BAF\({}_{\rm Zn}\)) in rice in farmland in central and eastern Guangxi, achieving a strong predictive effect with an R\({}^{2}\) of 0.93. [PERSON] et al. [16] used Fe\({}_{2}\)O\({}_{3}\), Mn, and SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) as the input variables for a maize prediction model and Fe\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\), and P for a rice prediction model. They applied a backpropagation neural network model to predict the BAF\({}_{\rm Zn}\) in maize and rice in Guang'an county, Sichuan Province, achieving good predictive results. Similarly, [PERSON] et al. [17] used a neural network algorithm and selected soil Zn, SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\), and K\({}_{2}\)O as the input variables to construct a predictive model for the wheat grain zinc content in the Weining Plain of Ningxia, with a good model fitting. Several scholars' studies have shown that, despite the differences in input variables and machine learning methods across different research regions, predicting the crop zinc content based on the major soil elements (including oxides) and TOC is feasible. However, in these studies, both the training and validation datasets were collected from the same research region.