## 3 Results
### Recall Confidence  
It is essential to understand that setting the appropriate confidence threshold depends on the specific application and the trade-off between missing detections (false negatives) and accepting false positives. Figure 6 shows how the recall confidence level decreases as the batch sizes are increased. The obtained recall confidence values for each of the batch sizes were 0.840, 0.820, 0.780, 0.790, 0.750, 0.830, 0.770, 0.840, and 0.77 for batch sizes 10, 20, 30, 40, 50, 60, 70, 80, and 90, respectively. The highest recall confidence was recorded at batch size 10 and 80. Figure 6: Recall confidence of batch size training and validation at (**a**) batch size 10, (**b**) batch size 20, (**c**) batch size 30, (**d**) batch size 40, (**e**) batch size 50, (**f**) batch size 60, (**g**) batch size 70, (**h**) batch size 80, and (**i**) batch size 90. The confusion matrices obtained for all the batch sizes are presented in Figure 7a-i. In the confusion matrices, the diagonal elements, running from the top left to the bottom right represent the number of true positive (TP) predictions for each class. Figure 7a presents the confusion matrix for batch size 10. Specifically, it shows that 67% of items in the \"banana\" class, 30% in the \"pepper\" class, 46% in the \"spinach\" class, 59% in the \"sugarcane\" class, and 6% in the \"weed\" class were correctly classified. Conversely, 33%, 70%, 54%, 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 are instances where the classifier could not confidently assign these objects to any specific class. Figure 7: _Cont._  
Tables 1-9 present the crop-specific performance of the model at different batch sizes. In these tables, the precision values span from 0 (indicating no precision) to 1 (perfect precision), while the recall values also range from 0 (no recall) to 1.0 (ideal recall). For batch size 10 (see Table 1), among the different classes 'banana' exhibited the highest precision at approximately 0.884, making it the most precise class. This was followed by'spinach', 'pepper crops','sugarcane crops', and finally 'weeds' with approximately 0.278 precision, ranking as the least precise. For recall, the 'banana' class achieved the highest recall value at approximately 0.778, making it the best recognized class. In descending order, the classes'sugarcane crops','spinach', 'pepper crops', and 'weeds' followed, with 'weeds' having the lowest recall value at approximately 0.118. This suggests that the classifier identified fewer positive samples of 'weeds' compared to'spinach', 'bananas', 'pepper crops', and'sugarcane'. \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 & 0.884 & 0.778 & 0.874 & 0.553 \\ Pepper & 52 & 10 & 0.536 & 0.578 & 0.577 & 0.226 \\ Spinach & 52 & 37 & 0.561 & 0.595 & 0.554 & 0.219 \\ Sugarcane & 52 & 37 & 0.29 & 0.676 & 0.431 & 0.15 \\ Weeds & 52 & 17 & 0.278 & 0.118 & 0.0962 & 0.0422 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the precision and recall obtained at batch size 10. Figure 7: Confusion Matrix for (**a**) batch size 10, (**b**) batch size 20, (**c**) batch size 30, (**d**) batch size 40, (**e**) batch size 50, (**f**) batch size 60, (**g**) batch size 70, (**h**) batch size 80, and (**i**) batch size 90. \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 & 0.758 & 0.700 & 0.879 & 0.517 \\ Pepper & 52 & 10 & 0.796 & 0.800 & 0.762 & 0.190 \\ Spinach & 52 & 37 & 0.595 & 0.597 & 0.580 & 0.240 \\ Sugarcane & 52 & 37 & 0.306 & 0.568 & 0.500 & 0.264 \\ Weeds & 52 & 17 & 0.425 & 0.118 & 0.162 & 0.0532 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Details of the precision and recall obtained at batch size 60.