## 2 Literature Review
The outcome demonstrated the Polynomial algorithm's superiority in predicting nutrient concentrations in rice levels. Other studies predicting nutrient content in rice used 16 nutrients as predictors, such as moisture, crude protein, fat, ash, total dietary fiber, soluble dietary fiber, insoluble dietary fiber, total sugar, sucrose, glucose, fructose, amylose, amylopectin, total amino acids, lysine, and thiamine [30]. These studies employed three algorithms: Stepwise Regression, PLSR, and MLR for prediction. The results favored stepwise regression analysis for its superior accuracy in predicting nutrient content in rice. Another study aimed to predict nutrient content in rice based on 14 nutrients, including moisture, crude protein, fat, ash, total dietary fiber, soluble dietary fiber, insoluble dietary fiber, total sugar, sucrose, glucose, fructose, amylose, amylopectin, and thiamine. This research compared three algorithms: Ridge Regression, Principal Component Regression (PCR), and PLSR. Ridge Regression stood out as the most effective method for predicting nutrient content in rice, delivering higher accuracy than PLSR and PCR. Utilizing another set of 14 nutrients, including moisture, crude protein, fat, ash, total dietary fiber, soluble dietary fiber, insoluble dietary fiber, total sugar, sucrose, glucose, fructose, amylose, amylopectin, and thiamine, as predictors for nutrient prediction in rice, another study employed three algorithms: MLR, PLSR, and Lasso Regression [33]. The experimental results highlighted the precision of the lasso regression algorithm in predicting both yield and nutrient contents in rice, offering potential benefits in optimizing rice crop cultivation and management. In a similar vein, another study [34; 35] compared three prediction algorithms, namely MLR, PLSR, and PCR, for nutrient content in rice, considering nutrients such as moisture, crude protein, fat, ash, total dietary fiber, soluble dietary fiber, insoluble dietary fiber, total sugar, sucrose, glucose, fructose, amylose, amylopectin, and thiamine. The findings indicated that MLR provided more accurate predictions compared to the other methods assessed. Table 1 provides a comparative analysis of the advantages and disadvantages of regression algorithms [26; 27; 28; 29; 30; 31; 32; 33; 34; 35] for rice nutrient prediction. These algorithms effectively capture both linear and nonlinear correlations among various nutrients. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Linear Regression Types** & **Proficiency** & **Advantage** & **Disadvantage** \\ \hline Simple Linear Regression (LR) [25] & Identifying the correlation between two variables & - & Computationally efficient - - Required fewer parameters & - & Unable to deal with nonlinearity - Sensitive to outlier \\  & & - & Able to deal with large number of features & - & Computationally expensive - Unsatisfactory results when the number of predictors is more than sample size \\  & & - & Ability to deal with small dataset & - & Computationally expensive - Overfit if the degree of polynomial is high \\  & & - & Provide balance between features and algorithms & - & Time demanding - Unstable due to overfitting \\  & & - & Able to deal with large dataset & - & Issue with finding optimal - value for lambda \\  & & - & Prevent overfitting & - & Challenging while dealing with large dataset \\ Lasso Regression [33] & Known as regularization method & - & Mitigate overfitting & - & Challenging while that has large number of observations \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantage and disadvantage of Linear Regression algorithm. These diverse regression algorithms collectively share a common aim: to enhance the precision and reliability of predictions concerning rice nutrient content, a critical step in optimizing fertilizer application, ensuring a balanced nutrient supply, and ultimately elevating rice crop quality and yield while reducing environmental impact.