## 2 Literature Review  
One of the promises of a digital twin in crop management is for the automatic prediction system to support in deciding the appropriate fertilization period [22; 23; 24]. Deploying the sensors which monitor the concentration of nutrients present in soil, humidity, and temperature in the real fields to make consistent quality checks. Machine learning could be used as a proactive measure as a predictor of the degradation of crop medium's and a crop's plant nutrients, which could increase the risk of crop pests and diseases [25; 26]. Regression algorithms play a central role in rice nutrient prediction by unraveling the intricate interplay of nutrients in rice cultivation. Elastic Net Regression (EN), Polynomial Regression (PN), Stepwise Regression (SW), Ridge Regression (RR), Lasso Regression (LS), and Linear Regression (LR) provide essential insights into the complex relationships among soil composition, environmental variables, and agricultural practices [27; 28; 29; 30]. These algorithms empower researchers to comprehend the often-nonlinear dependencies among these factors, deepening our understanding of how various nutrients influence rice nutrition. Regression algorithms are data-driven, offering a robust framework for analyzing and interpreting nutrient data from diverse sources. By harnessing historical data and observational insights, these algorithms provide crucial guidance on how different nutrients impact rice composition. This knowledge is vital for optimizing fertilizer usage, enhancing nutrient management, and ultimately improving rice quality and yields [27; 28; 29; 30]. These algorithms also aid farmers, agricultural experts, and policymakers in making informed decisions about crop management, fertilization strategies, and soil enrichment. This proactive approach helps in avoiding over-fertilization or under-fertilization, mitigating their detrimental effects on crop health and environmental sustainability [31; 32]. Existing works on rice nutrients have focused on predicting essential nutrient levels in rice, such as N, P, K, Mg, and Ca, and their effects on rice plant growth and development. One study employed an artificial neural network-based prediction algorithm to assess the influence of individual nutrients (N, P, K, Zn, and S) on various rice plant parameters. The algorithm indicated that optimal growth often occurs with nutrient doses below the maximum applied levels, while maximum yield is achieved at a 100% nutrient dose [22]. Another study used regression methods and found that random forest regression algorithms provided the highest accuracy for estimating rice shoot dry matter, leaf area index, and nitrogen accumulation [23]. A third study evaluated different approaches for estimating rice above-ground biomass, plant nitrogen uptake, and nitrogen nutrition index, with the Random Forest algorithm demonstrating a superior performance [25]. An additional study focused on using machine learning for the early detection of nutrient deficiency in rice through leaf image processing, achieving high testing accuracy and roc_auc score [8]. Rice nutrient content prediction, based on the composition of other nutrient information, including nitrogen, phosphorus, potassium, and organic matter as input variables, was addressed in a study [26]. This study compared the EN algorithm with traditional linear regression methods, including Ordinary Least Squares (OLS) Regression, Ridge Regression, and Lasso Regression. The results highlighted the superior performance of the EN algorithm, exhibiting higher R-squared scores (R2) and lower Mean Absolute Error (MAE). Thus, Elastic Net proves more accurate in predicting rice nutrient content and its correlation with other nutrients. Essential nutrient levels in rice can also be predicted using spectral data from remote sensing [28], considering nutrients like N, P, K, Mg, and Ca. This research compared the Polynomial Regression algorithm with two other methods: Multi Linear Regression (MLR) and Partial Least Squares Regression (PLSR).