## 4 Results and Discussion  
The performance of the best model extracted from the 10 different executions on the validation set are listed in Table 5, indicating a precision of 0.73 and a recall of 0.95, leading to an F1-score of 0.83 for the YOLOv3 model. YOLOv3-tiny showed minor results, with a precision of 0.60 and a recall of 0.71, leading to an F1-score of 0.65. These results are expected because of the small training dataset along with the reduced number of layers in the tiny version. An F1-score of 0.83 indicates that YOLOv3 has a good balance between the precision and recall for this dataset, showing its efficiency in identifying true positives while minimizing false positives. Based on that, real-world experiments were conducted using the strawberry plants in the greenhouse to verify the model's generalization performance. First, we performed an in-lab test using the tools shown in Figure 7, including the NVIDIA Jetson TX2 card, a web camera with a resolution of \(640\times 480\), a monitor for visualization, a keyboard, and a mouse. To assess the effectiveness of \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** \\ \hline YOLOv3 & 0.73 & 0.95 & 0.83 \\ \hline YOLOv3-tiny & 0.60 & 0.71 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: YOLOv3 and YOLOv3-tiny validation metric summary on the strawberry dataset. our implementation, the ONNX representation was used. To assess the accuracy, suitability, and consistency of the proposed model, three alternative contemporary scenarios were trained and tested on the same datasets. These scenarios included traditional YOLOv3, YOLOv3-tiny, and YOLOv3-tiny with ONNX. The experiment was performed using two types of data, i.e., video and live video. We encountered certain challenges with the robot, as it is still in the development stage. So, we conducted a simulation utilizing the tools illustrated in Figure 7, positioning the camera at an optimal altitude to cover two rows of the plantation. Table 6 summarizes numerical evidence on the performance of the model compared to the other scenarios. The model achieves an accuracy of more than 97% with a processing time of over 15 milliseconds per frame. The results shown in Table 6 demonstrate real-time plant detection with mAP above 96%, which reveals the effectiveness and utility of the proposed model. Moreover, it showcases superior performance compared with the other three scenarios for both types of data. However, when considering processing speed, YOLOv3-tiny exhibits potential for achieving faster speeds because of its optimal balance between detection speed and accuracy, particularly when utilizing the ONNX representation. Nevertheless, in applications such as robotic pesticide spraying, detection accuracy holds greater significance, as the robot must ensure precise spraying while avoiding substantial areas of the plants. Additionally, YOLOv3-tiny versions are characterized by their compact networks and fewer prediction stages, which may risk information loss on objects of various sizes, given the varying sizes of plants. This issue is particularly significant in our case, where we have a limited dataset to train the model. Furthermore, when assessing robot navigation speed, a difference of 2 FPS may not be as critical as an approximate 6% increase in accuracy. It is important to acknowledge that these results were obtained by running the models using the NVIDIA Jetson TX2 board and not a computer. Figure 8 shows some online real-time detection in the greenhouse environment covering different day illumination conditions in the morning and afternoon.