## 4 Experimental Results and Discussion
### Vision Capabilities Simulations
#### 4.2.4 Comparison with State of the Art  
In order to have a standardized basis for comparison and to assess the performance of the **608@VOC+WA-WGISD** against the state of the art, two distinct exploratory studies have been conducted. First, the recognition capabilities of **608@VOC+WA-WGISD** have been validated with two well-known datasets; that is to say, the Grapevine Bunch Detection Dataset version 2 (GBDDv2, [[https://zenodo.org/records/7717055](https://zenodo.org/records/7717055)]([https://zenodo.org/records/7717055](https://zenodo.org/records/7717055)), accessed on 21 March 2024), and the Open Image Dataset version 7 (OIDv7, [[https://storage.googleapis.com/openimages/web/visualizer/index.html](https://storage.googleapis.com/openimages/web/visualizer/index.html)]([https://storage.googleapis.com/openimages/web/visualizer/index.html](https://storage.googleapis.com/openimages/web/visualizer/index.html)), accessed on 4 April 2024). Table 5 reports the results against the evaluation and test splits for both datasets. The choice to conduct several tests with different IoU thresholds is motivated by the different bounding box definition strategy adopted for GBDDv2 and OIDv7 as compared to WA-WGISD. In fact, the first two datasets provide, in general, wider bounding boxes to determine the position of each grapevine bunch in the images, whilst the WA-WGISD applies a more conservative policy (i.e., narrower bounding boxes). For this reason, adopting lower IoU thresholds to classify TPs, FPs and FNs represents a legitimate choice for an unbiased comparison against these datasets. Being created specifically for grapevine bunch detection, the GBDDv2 evaluation and test sets have been used in their entirety for the evaluation round. In this regard, the numbers reported in the first and second rows of Table 5 show a remarkable recognition behaviour, which far outperforms both the precision and F1-score metrics as calculated against the evaluation and test splits of the VOC+WA-WGISD for the \"grapes\" class, while keeping the recall almost aligned with the expected outcome. On the other hand, some clarifications are required to correctly explain the decrease in the detection performances over the OIDv7, which has been preemptively framed to the Grape class (third and forth rows of Table 5). Notice that the **608@VOC+WA-WGISD** has been trained with the purpose of detecting whole grape clusters in the vineyard (bunches) and not single grape berries, whilst the Grape class of the OIDv7 considers bounding boxes for all of the following:  
* Single grape berries; \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **IoU** & **Conf. Threshold** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{GBDDv2} & \multirow{2}{*}{Evaluation} & 0.40 & 0.05 & 0.94 & 0.79 & 0.86 \\  & & 0.45 & 0.05 & 0.86 & 0.77 & 0.81 \\  & & 0.50 & 0.05 & 0.78 & 0.75 & 0.77 \\ \cline{2-8}  & \multirow{2}{*}{Test} & 0.40 & 0.05 & 0.96 & 0.82 & 0.89 \\  & & 0.45 & 0.05 & 0.90 & 0.81 & 0.85 \\  & & 0.50 & 0.05 & 0.82 & 0.80 & 0.81 \\ \hline \multirow{4}{*}{OIDv7} & \multirow{2}{*}{Evaluation} & 0.40 & 0.05 & 0.68 & 0.28 & 0.39 \\  & & 0.45 & 0.05 & 0.48 & 0.21 & 0.29 \\ \cline{1-1}  & & 0.50 & 0.05 & 0.48 & 0.21 & 0.29 \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{Test} & 0.40 & 0.05 & 0.61 & 0.28 & 0.39 \\  & & 0.45 & 0.05 & 0.56 & 0.27 & 0.36 \\  & & 0.50 & 0.05 & 0.52 & 0.25 & 0.34 \\ \hline \multirow{4}{*}{OIDv7} & \multirow{2}{*}{Evaluation} & 0.40 & 0.05 & 0.64 & 0.30 & 0.40 \\  & & 0.45 & 0.05 & 0.46 & 0.23 & 0.31 \\ \cline{1-1}  & & 0.50 & 0.05 & 0.46 & 0.23 & 0.31 \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{Test} & 0.40 & 0.05 & 0.60 & 0.48 & 0.53 \\ \cline{1-1}  & & 0.45 & 0.05 & 0.56 & 0.47 & 0.51 \\ \cline{1-1}  & & 0.50 & 0.05 & 0.53 & 0.45 & 0.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detection results of **608@VOC+WA-WGISD** over GBDDv2 and OIDv7 (restricted to Grape class), with confidence threshold that optimizes F1-score.