## 3 Results
### Recall Confidence
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Class** & **Images** & **Instances** & **Precision** & **Recall** & **mAP50** & **mAP50-95** \\ \hline Banana Tree & 52 & 9 & 0.882 & 0.778 & 0.833 & 0.571 \\ Pepper & 52 & 10 & 0.503 & 0.609 & 0.695 & 0.237 \\ Spinach & 52 & 37 & 0.669 & 0.622 & 0.617 & 0.246 \\ Sugarcane & 52 & 37 & 0.439 & 0.529 & 0.445 & 0.195 \\ Weeds & 52 & 17 & 0.485 & 0.0588 & 0.0923 & 0.0437 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Details of the precision and recall obtained at batch size 40. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Class** & **Images** & **Instances** & **Precision** & **Recall** & **mAP50** & **mAP50-95** \\ \hline Banana Tree & 52 & 9 & 0.882 & 0.778 & 0.833 & 0.571 \\ Pepper & 52 & 10 & 0.503 & 0.609 & 0.695 & 0.237 \\ Spinach & 52 & 37 & 0.669 & 0.622 & 0.617 & 0.246 \\ Sugarcane & 52 & 37 & 0.439 & 0.529 & 0.445 & 0.195 \\ Weeds & 52 & 17 & 0.485 & 0.0588 & 0.0923 & 0.0437 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Details of the precision and recall obtained at batch size 30. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Class** & **Images** & **Instances** & **Precision** & **Recall** & **mAP50** & **mAP50-95** \\ \hline Banana Tree & 52 & 9 & 0.857 & 0.665 & 0.777 & 0.540 \\ Pepper & 52 & 10 & 0.465 & 0.600 & 0.495 & 0.129 \\ Spinach & 52 & 37 & 0.519 & 0.703 & 0.660 & 0.268 \\ Sugarcane & 52 & 37 & 0.344 & 0.568 & 0.428 & 0.209 \\ Weeds & 52 & 17 & 0.292 & 0.176 & 0.201 & 0.0628 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Details of the precision and recall obtained at batch size 50. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Class** & **Images** & **Instances** & **Precision** & **Recall** & **mAP50** & **mAP50-95** \\ \hline Banana Tree & 52 & 9 & 0.882 & 0.778 & 0.833 & 0.571 \\ Pepper & 52 & 10 & 0.503 & 0.609 & 0.695 & 0.237 \\ Spinach & 52 & 37 & 0.669 & 0.622 & 0.617 & 0.246 \\ Sugarcane & 52 & 37 & 0.439 & 0.529 & 0.445 & 0.195 \\ Weeds & 52 & 17 & 0.485 & 0.0588 & 0.0923 & 0.0437 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Details of the precision and recall obtained at batch size 40. The confusion matrix obtained at batch size 20 for the multi-class classification is illustrated in Figure 7b. Specifically, it shows that 44% of items in the \"banana\" class, 80% in the \"pepper\" class, 57% in the \"spinach\" class, 59% in the \"sugarcane\" class, and 6% in the \"weed\" class were correctly classified. Conversely, 56%, 20%, 43%, 41%, and 88% of objects belonging to the banana, pepper, spinach, sugarcane, and weed classes, respectively, were classified as \"unknown.\"  
As shown in Table 2, \"banana' also exhibited the highest precision at approximately 0.826, making it the most precise. This was followed by 'pepper crops', 'weeds', and'spinach', in that order, with'sugarcane' ranking as the least precisely detected with approximately 0.511 precision. Notably, the 'pepper' class achieved the highest recall, with an approximate value of 0.634. In descending order,'sugarcane crops', banana','spinach', and finally 'weeds', with an approximate recall value of 0.118 followed, indicating that the classifier identified fewer positive instances of'sugarcane','spinach', 'bananas', and 'pepper crops' and the least positive instances of 'weeds'. The confusion matrix obtained at batch size 30 for the classification (see Figure 7c) shows that 78% of items in the \"banana\" class, 60% in the \"pepper\" class, 65% in the \"spinach\" class, 59% in the \"sugarcane\" class, and 6% in the \"weed\" class were correctly classified. Conversely, 22%, 40%, 35%, 41%, and 94% of objects belonging to the banana class, pepper class, spinach class, sugarcane class, and weed class, respectively, were classified as \"unknown.\" These instances could not be confidently assigned to any specific class by the classifier. Table 3 shows that 'banana' exhibited the highest precision at approximately 0.882, making it the most precise.