## 3 Results and Discussion  
### AOC Concentration in Radnapuram, Tirunelveli District  
In this research, the selected spectral variables were used as independent variables, while the soil chemical nutrients (AOC, AN, AP, and AK) were used as dependent variables [20; 21]. The correlation coefficient and relative root mean square error were used to assess the accuracy of the estimation models by approximating the soil AOC, AN, AP, and AK contents. The mean absolute deviation and present fundamental error were calculated \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Datasets** & **R-Squared** & **Root Mean Squared Error** & **Mean Squared Error** & **Mean Absolute Percent Error** \\ \cline{2-5}  & & **(R2)** & **(RMSE)** & **(MSE)** & **(MAPE)** \\ \hline \multirow{2}{*}{AOC} & Train & 100.00\% & 0 & 0 \\ \cline{2-5}  & Test & 97.50\% & 0.124 & 0.0154 & 0.04 \\ \hline \multirow{2}{*}{AN} & Train & 99.99\% & 0.0005 & 0 & 0.0026 \\ \cline{2-5}  & Test & 99.98\% & 0.0007 & 0 & 0.0039 \\ \hline \multirow{2}{*}{AP} & Train & 51.02\% & 8.4924 & 72.1203 & 0.2286 \\ \cline{2-5}  & Test & 49.93\% & 8.5864 & 73.7256 & 0.2294 \\ \hline \multirow{2}{*}{AK} & Train & 26.72\% & 213.7068 & 45,670.578 & 0.3795 \\ \cline{2-5}  & Test & 25.06\% & 216.123 & 46,709.154 & 0.3865 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Regression tree predictions for AOC, AN, AP, and AK values. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Soil Nutrient** & **Selected Spectral Variables** & **Pearson Correlation Coefficients** \\ \hline \multirow{2}{*}{AOC} & R353, R1640, FD642, FD1676, SD392, & \(-\)0.34 **, 0.26 **, 0.47 **, 0.35 **, 0.34 **, \\  & SD1245 & & \(-\)0.26 ** \\ \hline AN & R1256, FD796, FD1122, FD2132, SD556 & \(-\)0.23 **, 0.25 **,0.36 **, 0.45 **, 0.43 ** \\ \hline AP & R2245, R2486, FD1545, FD1702, SD657 & 0.14 **, 0.50 **, 0.45 **, \(-\)0.26 **, 0.32 ** \\ \hline AK & R1498, FD542, SD1635, SD2456 & 0.24 **, 0.47 **, \(-\)0.36 **, 0.33 **, 0.28 ** \\ \hline \hline \end{tabular}  
* Indicates significant correlation at (\(p<0.01\)) level.  
\end{table} 
Table 3: The best spectral variables to predict four soil nutrients. for the training and testing datasets. Table 5 shows the importance of different optimization techniques used and compares the same. As represented in Table 5, the soil nutrient predictions were carried out using different bands including visible bands, near-infrared bands, VNIR+NIR region bands, and bands developed using 150 random bands [22; 23]. The AOC, AN, AP, and AK contents of the soil were predicted and the PLSR model was validated. Table 6 shows the AOC prediction for different bands for the PLSR prediction model [22; 24]. The results show the best performance using the PLSR methods and VNIR+NIR regions because R2 is 0.970, as shown in Figure 5.  
In the proposed method, the regression trees divide the data into different groups. As shown in Equation (6), the algorithm divides the space using rectangles to make the data easier to read and to find boxes that reduce the RSS (residual sum of squares). \[Regression=\sum_{j=1}^{J}\sum_{i=R^{j}}(y_{i}-\hat{y}_{R_{j}})^{2} \tag{6}\]  
The partitioning of the feature space into j boxes was not computationally feasible. This is addressed using recursive binary splinting, a top-down greedy technique. The top-down approach, which begins at the top of the tree, divides the predictor space into two new branches at each stage, as illustrated in Figure 6. AOC is divided into three levels based on spectral reflectance, whereas AN, AP, and AK are divided into two. AOC seems to have a mean of 0.8307 and a standard deviation (STD) of 0.3749 from 65 soil samples, which will