## 4 Experimental Validation  
The proposed model is simulated using the Python 3.6.5 tool. The proposed model is experimented on PC i5-8600k, GeForce 1050 Ti 4 GB, 16 GB RAM, 250 GB SSD, and 1 TB HDD. The parameter settings are given as follows: learning rate: 0.01, dropout: 0.5, batch size: 5, epoch count: 50, and activation: ReLU. The weed detection results of the MBMODL-WD model are tested using a dataset, compassing 3000 images. The dataset holds 287 crop images and 2713 weed images, as defined in Table 1. Figure 2 illustrates some sample images. The confusion matrices of the MBMODL-WD model on the weed detection process are demonstrated in Figure 3. The outcomes displaying the MBMODL-WD model properly recognized the crop and weed images under all aspects. \begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **No. of Images** \\ \hline Crop & 287 \\ \hline Weed & 2713 \\ \hline
**Total Number of Images** & **3000** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset details. Figure 2: Sample weed images. Table 2 and Figure 4 report the weed detection results of the MBMODL-WD method on 60% of TR and 40% of TS databases. The simulation values revealed the MBMODL-WD method properly classified the crop and weed images. On 60% of TR database, the MBMODL-WD model reached an average \(accu_{bal}\) of 94.73%, \(prec_{n}\) of 93.75%, \(rec_{n}\) of 94.73%, \(F_{score}\) of 94.23%, MCC of 88.48%, and \(G_{mean}\) of 94.64%. Concurrently, on 40% of TS database, the MBMODL-WD approach gained an average \(accu_{bal}\) of 92.73%, \(prec_{n}\) of 97.40%, \(rec_{n}\) of 92.73%, \(F_{score}\) of 94.91%, MCC of 90.01%, and \(G_{mean}\) of 92.47%. Figure 3: Confusion matrices of MBMODL-WD system, (**a**,**b**) TR and TS databases of 60:40, and (**c**,**d**) TR and TS databases of 70:30. Table 3 and Figure 5 depict the weed detection results of the MBMODL-WD system on 70% of TR and 30% of TS databases. The simulation outcomes stated that the MBMODL-WD system properly classified the crop and weed images. On 70% of TR database, the MBMODL-WD system reached an average \(accu_{bal}\) of 98.30%, \(prec_{n}\) of 97.87%, \(reca_{1}\) of 98.30%, \(F_{score}\) of 98.08%, MCC of 96.17%, and \(G_{mean}\) of 98.29%. Simultaneously, on 30% of TS database, the MBMODL-WD algorithm attained an average \(accu_{bal}\) of 98.99%, \(prec_{n}\) of 96.13%, \(reca_{1}\) of 98.99%, \(F_{score}\) of 97.51%, MCC of 95.08%, and \(G_{mean}\) of 98.99%. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Class** & \(Accu_{bal}\) & \(Prec_{n}\) & \(Reca_{l}\) & \(F_{score}\) & MCC & \(G_{mean}\) \\ \hline \multicolumn{8}{l}{**Training Phase (60\%)**} \\ \hline Crop & 90.62 & 88.41 & 90.62 & 89.51 & 88.48 & 94.64 \\ \hline Weed & 98.84 & 99.08 & 98.84 & 98.96 & 88.48 & 94.64 \\ \hline
**Average** & **94.73** & **93.75** & **94.73** & **94.23** & **88.48** & **94.64** \\ \hline \multicolumn{8}{l}{**Testing Phase (40\%)**} \\ \hline Crop & 85.83 & 96.46 & 85.83 & 90.83 & 90.01 & 92.47 \\ \hline Weed & 99.63 & 98.34 & 99.63 & 98.98 & 90.01 & 92.47 \\ \hline
**Average** & **92.73** & **97.40** & **92.73** & **94.91** & **90.01** & **92.47** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Weed detection outcome of MBMODL-WD system under 60:40 of TR/TS databases. Figure 4: Average weed classification analysis of MBMODL-WD system under 60:40 of TR/TS databases. The TACC and VACC of the MBMODL-WD approach are investigated on weed detection performance in Figure 6. The figure states that the MBMODL-WD methodology revealed an improved performance with higher values of TACC and VACC. It is evident that the MBMODL-WD system has attained superior TACC outcomes.