## 4 Experimental Validation
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Class** & _Acctual_ & _Precn_ & _Reca1_ & _Fscore_ & **MCC** & _Gmean_ \\ \hline \multicolumn{6}{l}{**Training Phase (70\%)**} \\ \hline Crop & 97.01 & 96.06 & 97.01 & 96.53 & 96.17 & 98.29 \\ \hline Weed & 99.58 & 99.68 & 99.58 & 99.63 & 96.17 & 98.29 \\ \hline
**Average** & **98.30** & **97.87** & **98.30** & **98.08** & **96.17** & **98.29** \\ \hline \multicolumn{6}{l}{**Testing Phase (30\%)**} \\ \hline Crop & 98.84 & 92.39 & 98.84 & 95.51 & 95.08 & 98.99 \\ \hline Weed & 99.14 & 99.88 & 99.14 & 99.51 & 95.08 & 98.99 \\ \hline
**Average** & **98.99** & **96.13** & **98.99** & **97.51** & **95.08** & **98.99** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Weed detection outcome of MBMODL-WD system under 70:30 of TR/TS databases. Figure 5: Average weed classification analysis of MBMODL-WD system under 70:30 of TR/TS databases. The TLS and VLS of the MBMODL-WD methodology are tested on weed detection performance in Figure 7. The figure points out that the MBMODL-WD approach has exposed optimum performance with decreased values of TLS and VLS. It is noticeable that the MBMODL-WD method has resulted in lesser VLS outcomes. Figure 6: TACC and VACC analyses of MBMODL-WD system on weed classification. Figure 7: TLS and VLS analyses of MBMODL-WD system on weed classification. A noticeable precision-recall study of the MBMODL-WD system under test database is represented in Figure 8. The figure pointed out that the MBMODL-WD algorithm has enhanced values of precision-recall values in various classes. A comprehensive ROC investigation of the MBMODL-WD methodology under test database is depicted in Figure 9. The results denoted the MBMODL-WD algorithm has displayed its capability in categorizing in various classes. Figure 8: Precision–recall analysis of MBMODL-WD system. Figure 9: ROC curve analysis of MBMODL-WD system. Table 4 reports a detailed weed detection result of the MBMODL-WD model and existing models [22]. Figure 10 examines the \(prec_{n}\) and \(reca_{l}\) results of the MBMODL-WD method and recent methods. The results demonstrated the MBMODL-WD method reached enhanced values of \(prec_{n}\) and \(reca_{l}\). For instance, based on \(prec_{n}\), the MBMODL-WD model has shown a higher \(prec_{n}\) value of 96.13%. In contrast, the RF, KNN, SVM, ResNet-101, VGG-16, SVM-Pixel-based, and HLBDDL-WDSA models have shown reduced \(prec_{n}\) of 95.24%, 62.43%, 91.32%, 93.15%, 93.96%, 85.41%, and 95.84%, respectively. In addition, in terms of \(reca_{l}\), the MBMODL-WD approach has exposed a maximally \(reca_{l}\) value of 98.99%. In contrast, the RF, KNN, SVM, ResNet-101, VGG-16, SVM-Pixel-based, and HLBDDL-WDSA models have shown lesser \(reca_{l}\) values of 93.93%, 61.85%, 91.24%, 94.47%, 92.76%, 85.79%, and 95.18%, correspondingly. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & \(Acc_{bal}\) & \(Prec_{n}\) & \(Reca_{l}\) & **MCC** & \(G_{mean}\) \\ \hline MBMODL-WD & 98.99 & 96.13 & 98.99 & 95.08 & 98.99 \\ \hline RF Model & 95.53 & 95.24 & 93.93 & 88.63 & 89.87 \\ \hline KNN Model & 62.81 & 62.43 & 61.85 & 35.83 & 93.29 \\ \hline SVM Model & 94.39 & 91.32 & 91.24 & 84.00 & 90.34 \\ \hline ResNet-101 Model & 93.52 & 93.15 & 94.47 & 92.90 & 92.80 \\ \hline VGG-16 Model & 93.29 & 93.96 & 92.76 & 92.93 & 89.07 \\ \hline SVM-Pixel-based & 85.84 & 85.41 & 85.79 & 86.81 & 93.62 \\ \hline HLBDDL-WDSA & 98.96 & 95.84 & 95.18 & 93.34 & 95.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of MBMODL-WD system with other algorithms. Figure 10: \(Prec_{n}\) and \(reca_{l}\) analyses of MBMODL-WD system with other weed classification algorithms. Figure 11 observes the MCC and \(G_{mean}\) results of the MBMODL-WD algorithm and recent approaches. The outcomes exhibited that the MBMODL-WD system gained improved values of MCC and \(G_{mean}\). For instance, with respect to MCC, the MBMODL-WD model outperformed a maximal MCC value of 95.08%.