## 3 Materials and Methods
### Dataset Description  
A self-collected rice dataset was used as described in Table 2, comprising 348 observations and nine attributes. This multivariate dataset features a combination of categorical and numerical data, including spatiotemporal factors such as Season, Day, Plot, and Subplot. The _Season_ attribute categorizes data into two distinct seasons, denoted by the values 1 and 2, enabling the exploration of how seasonal changes influence rice nutrient levels, a fundamental aspect of rice production optimization. Additionally, the _Day_ attribute, with three distinct values, 30, 60, and 90, introduces temporal granularity, facilitating an examination of nutrient content variations within each season. This temporal dimension is essential for understanding the influence of specific days on nutrient levels. Furthermore, the _Plot_ attribute categorizes data into four distinct plot locations represented by values 1, 3, 4, and 5, enabling the assessment of nutrient distribution across different areas within the study site, thus adding a spatial context to the analysis. _Subplot_ further refines the spatial information by specifying 15 sublocations within each plot, denoted by values such as 1A, 1B, 1C, and so forth. This fine-grained attribute is invaluable for scrutinizing nutrient variation within specific subregions of the plots, enhancing spatial precision. Additionally, the dataset incorporates nutrient concentration, composition, and co-existence (\"N%\", \"P%\", \"K%\", \"Mg%\", \"Ca%\"), which is vital for understanding rice growth and health. The dataset's integrity is maintained, as it contains no missing values. An example of the data content is shown in Figure 1, which shows the concentration of each nutrient based on the spatial information. The best range of the nutrients are N: [1.17, 2.47], P: [0.25, 0.3], K: [1.85, 2.52], Mg: [0.11, 0.17], and Ca: [0.23, 0.33], which has produced the maximum weight grain at the planting plot with range [29.26, 39.42] at the end of the planting cycle. These values are considered the best practice to guide the intervention plan for the user (farmer or scientist). \begin{table}
\begin{tabular}{c c} \hline \hline
**Name of Dataset** & _Rice Dataset_ \\ \hline Dataset Characteristics & Multivariate \\ Attribute Characteristics & Categorical Data (Nominal), Numerical and Continual Data \\ Number of Instances & 348 \\ Attributes Number & 9 \\ Missing Values & No \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Rice dataset descriptions. Figure 2 shows the dashboard that presents the average rice nutrient concentration across the growth period and the rice anatomical values at harvesting time, while Figure 3 shows the nutrient value distribution. From Figure 2, we can identify the relationship of the nutrient con-existence, composition, and concentration with the yield. The digital twin supports a three-staged insight for crop intelligence. First, we could also see the average values of nutrients that have led to the yield, and the nutrient values from the plant with the best yield become the benchmark. So, this has motivated us towards the second intelligence by predicting the co-existence, concentration, and composition of the plant at each plot and subplot to know about their health. The third intelligence is nutrient recovery during the growth as an intervention mechanism, so that the predicted values can be a guide on precise additional nutrients to be added into the crop medium to optimize the yield. The precision of values for additional nutrients can mitigate unnecessary excess in fertilizer usage and waste pollution. The nutrient concentration distribution, as depicted in Table 3, highlights the range of values for the key nutrients N (%), P (%), K (%), Mg (%), and Ca (%) that is essential for agricultural productivity. The minimum (MIN) and maximum (MAX) values illustrate the variability in nutrient levels, emphasizing the complexity of nutrient dynamics in agriculture. Standard deviation (STDEV) values quantify the degree of variability around the mean.