## 1 Introduction
The process-oriented nature, mechanism analysis, and temporal dynamics of CGMs are crucial for understanding the intricate behaviors of agricultural production systems [25]. To delve further into the latest developments concerning CGMs' application in data assimilation, we conducted a literature review focusing on key terms like \"data assimilation\", \"Crop Growth Models\", and \"precision agriculture\". The results revealed the frequency distribution of various models in agricultural applications, as depicted in Figure 1. Evident from the figure, the SWAP model occupies a leading position in assimilation applications with an adoption rate exceeding 30%, while EPIC, WOFOST, and APSIM models are also widely used. On another front, the most recent CGMs research in agricultural Data Assimilation (DA) emphasizes the focus on precise, detailed, and integrated simulation of crop growth processes, aiming to achieve meticulous operations for crop growth status, yield, and even the entire agricultural production system [29]. Table 1 summarizes several common CGMs, showcasing their exceptional applicability in the domain of precision agriculture. To a certain extent, CGMs successfully complement the limitations of RS technology in acquiring and processing crop parameter information, especially in elucidating crop growth mechanisms, simulating crop responses under complex environmental conditions, and improving the accuracy and consistency of data interpretation [12; 30]. For instance, multiple studiesexplicitly highlight that in critical areas such as grain yield forecasting, crop fine-scale management, and regional optimization of agricultural resource allocation, CGMs are gradually emerging as vital instruments for assessing and investigating the performance of agricultural production systems [25; 26; 31; 32]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Application of Agricultural Assimilation** & **References** \\ \hline APSIM & \begin{tabular}{c} Simulate crop growth, soil water, and nutrient dynamics and crop \\ management practices (suitable for crops such as wheat, corn, and soybeans) \\ \end{tabular} & [33] \\ \hline Agromet Shell & \begin{tabular}{c} Simulated crop growth and yield (Prediction of crop growth and yield \\ changes based on meteorological data) \\ \end{tabular} & [34] \\ \hline Aquacrop & Response of simulated crops to water stress and yield prediction (Suitable for \\  & arid and semi-arid areas) & [35] \\ \hline CERES-wheat & \begin{tabular}{c} Simulate the growth and yield of wheat (Taking into account a variety of \\ environmental and regulatory factors) \\ \end{tabular} & [36] \\ \hline CROPGRO-Soybean & \begin{tabular}{c} Simulate crop growth and yield, such as soybeans (The physiological and \\ ecological processes of crops, soil conditions, and management measures \\ were considered.) \\ \end{tabular} & [37] \\ \hline Cropsyst & \begin{tabular}{c} Simulate the growth, development, and yield of a variety of crops (Suitable \\ for different climate and soil conditions) \\ \end{tabular} & [38] \\ \hline DAISY & \
\begin{tabular}{c} Simulate the interaction between soil moisture, nutrients, and crop growth \\ (Suitable for farmland and paddy field management) \\ \end{tabular} & [39] \\ \hline DSSAT &
\begin{tabular}{c} Simulate the growth, yield, and soil dynamics of many crops (Taking into \\ account the impact of climate change and management practices on crops) \\ \end{tabular} & [40] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Crop growth models commonly used for data assimilation and list of precision agriculture applications. Figure 1: Application of crop model data assimilation in Precision Agriculture Distribution Statistics of Frequency variation with time. However, despite their excellent performance in theoretical explorations and small-scale applications, CGMs encounter considerable challenges when scaling up to broader geographical or regional levels.