## 3 Results and Discussion
### AOC Concentration in Radnapuram, Tirunelveli District
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimization Techniques** & **RMSE** & **R-Squared** & **MSE** & **MAE** \\ \hline PSO & 0.152 & 0.890 & 0.023 & 0.124 \\ \hline CSO & 0.136 & 0.910 & 0.019 & 0.116 \\ \hline GWO & 0.154 & 0.870 & 0.024 & 0.129 \\ \hline MFO & 0.106 & 0.840 & 0.011 & 0.086 \\ \hline Hybrid Optimization & 0.182 & 0.970 & 0.033 & 0.157 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: AOC prediction for different bands for the PLSR prediction model with different optimization techniques. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Optimization Algorithms** & **Running Time** & **Important Parameter** & **Convergence for Hyperspectral Band Selection** & **Iteration for Obtaining Global Minimum** & **Accuracy for Selecting Optimal Relevant Hyperspectral Bands** \\ \hline \multirow{4}{*}{**PSO**[5] & Low & Learning factor, number of dimensions, range of particles, Vmax max change of particle velocity & Moderate & 1100 & High \\ \cline{1-1}  & & Population size, mutation, and crossover probability & Slow & 450 & Moderate \\ \hline
**CSO**[5] & High & Number of nests, Levy flight solution & Fast & 400 & Moderate \\ \hline
**FPO**[5] & Very High & Number of flowers & Very Fast & 150 & Very High \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of proposed work with PSO, GAO, and CSO. be divided into two levels based on the threshold value of 151.2. If the nitrogen content is less than 151.2, it is mapped to a low value and, if it is greater than 151.2, it is mapped to a medium value which is shown in Figure 6.  
Figure 5: Prediction of AOC for different band selections. Figure 6: Prediction of AOC for different band selections. (**a**) AOC split into three different levels (low, medium, high). (**b**) AN split into two different levels (low, medium). (**c**) AP split into two different levels (low, medium). (**d**) AK split into two different levels (low, medium). In the case of AOC, the mean is 0.5692 and the standard deviation is 0.7838. These three levels are separated by a threshold value of 0.4824. If the organic carbon content is less than 0.4824, it is classified as low. Otherwise, it is assigned to a different category. These categories are classified into two levels: medium and high. If the organic carbon value is less than 0.708453, it is assigned to medium weights while the other is assigned to high weights. Out of 65 samples, 40 are low value, 13 are medium value, and the remaining 12 are high value. In terms of phosphorus, the mean of 65 soil samples is 24.954 and the standard deviation is 12.134 [25,26]. AP is divided into two levels, which are distinguished by their phosphorus content. If the phosphorus value is less than 1.5, it is assigned a low value; otherwise, it is assigned a high value. In terms of potassium, the mean of 65 soil samples is 437.500 and the standard deviation is 249.650. AK is classified into two levels based on its potassium content [27,28]. If the potassium value is less than 1.5, it is assigned a low value; otherwise, it is assigned a high value. Figure 7 depicts the classification of AOC, AN, AP, and AK. The PLSR approach for soil spectroscopic analysis was evaluated using hyperspectral data from soil samples obtained in a laboratory, to estimate the amounts of the soil nutrients AOC, AN, AP, and AK. The soil nutrients could be predicted using RGB bands, near-infrared, individual RGB, and the VNIR+NIR region, according to the results [29,30]. The PLSR method performed best for combined VNIR+NIR regions, with an R2 value of 0.970, indicating that nutrients can be better predicted, and the PRISMA hyperspectral images were also used to evaluate lab-driven models in the area to increase soil nutrient content on a regional scale. PLSR approaches indicate the RMSE values for all the soil nutrients in the validation datasets and AOC has the best impact on HSI images.