## 5 Conclusions and Future Work  
The crop digital twin offers a revolution to monitor and intervene in crop health management. The physical twin surveils the condition of the crop, and this information can be analyzed by the digital twin to provide suggestions for countermeasures, such as nutrient enrichment to increase concentration levels. Predicting nutrient levels is crucial for optimizing fertilizer usage and ensuring a balanced nutrient supply, leading to higher-quality and increased yields, and reduced environmental impact. The importance of accurately anticipating essential nutrients, such as nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg), in rice cannot be overstated, as it directly impacts crop yield, quality, and environmental sustainability. The challenges in this field stem from the complexities introduced by the variability in nutrient content, the diversity of analytical approaches, data availability constraints, genetic diversity, and the associated costs and time investments. To address these challenges, this research has presented two approaches, namely, (i) single-nutrient concentration prediction and (ii) nutrient composition concentration prediction, to explore a range of regression algorithms, including Elastic Net Regression, Polynomial Regression, Stepwise Regression, Ridge Regression, Lasso Regression, and Linear Regression, to predict rice nutrient content. These algorithms have proven to be invaluable tools for capturing both linear and nonlinear correlations among various nutrients, offering a structured, data-driven approach to understanding and managing the complexities of rice nutrition. The findings reveal that the Polynomial Regression algorithm consistently outperforms the other models for predicting several nutrients, particularly calcium (Ca%), potassium (K%), phosphorus (P%), and nitrogen (N%). This algorithm's ability to handle both small and large datasets, along with its proficiency in capturing nonlinear relationships, makes it a favorable choice for optimizing nutrient management practices. It is important to note, however, that Model 2, focused on predicting magnesium (Mg%), demonstrated a unique characteristic, as Linear Regression outperformed Polynomial Regression. The dashboard in the digital twin visualizes the current nutrient content of the crop as a surveillance mechanism, while the predicted nutrient concentration is a valuable insight for precise fertilization to be added for nutrient recovery. This may mitigate fertilization  
Figure 12: Features importance for N% nutrient concentration prediction. overload and waste pollution. Although, this research currently addresses manual intervention, the implementation of the regression method supports the development of a low-resourced crop digital twin, enabling fast computations. In summary, these regression models provide essential insights into rice nutrient prediction, offering a pathway to optimize fertilizer use, ensure balanced nutrient supply, enhance rice quality, and reduce environmental impact. They contribute to the development of standardized methodologies for nutrient prediction and promote more sustainable and environmentally friendly rice cultivation practices. The choice of the most suitable regression model depends on the specific characteristics of the dataset and the nature of the nutrient interactions. Therefore, the selection of the appropriate algorithm is pivotal to achieving the highest predictive accuracy for rice nutrient content. Conceptualization, A.G. and N.M.S. ; Methodology, A.G.; Formal Analysis, and Writing--Original Draft Preparation, A.G.; Writing--Review & Editing, N.M.S. ; Creating visual representations of data, N.M.S. ; Supervision, and Funding Acquisition, N.M.S. ; Planning and designing the model, and gathering the data lake, S.K.B. and L.S.L. All authors have read and agreed to the published version of the manuscript.