## 2 Materials and Methods
### Deep Learning Model  
While YOLO models themselves are well-established, this work innovatively integrates these models into an already existing pesticide-spraying robot designed for the complex greenhouse environment. This integration is non-trivial and requires addressing unique challenges related to real-time object detection, decision-making, and precise actuation in dynamic greenhouse settings. These challenges include the robot's autonomous navigation system, the plants' orchard environment, and pesticide actuation control. Additionally, several other challenges must be considered because of their direct impact on the proposed application, such as the lack of a sufficient training dataset, the choice of the loss function to improve performance, and the implementation using a single GPU NVIDIA Jetson TX2 board. These constraints limited our choices to versions that are stable, easily embedded, and exhibit excellent performance in instant decision-making. Nowadays, there are 10 versions of YOLO, and the newest versions show a fast yet accurate performance, making them highly suitable for real-time applications [35]. However, earlier versions of YOLO still offer distinct advantages, such as simplicity and resource efficiency, that make them better candidates in applications with limited computational resources such as edge devices [36]. To select the most suitable YOLO version for this study, a comparative analysis was performed by comparing the most-used version in the embedded system (3, 4-tiny, 5-small, 5-medium) using NVIDIA Jetson TX2. This comparison took into  
Figure 1: AgriEco Robot platform. consideration the model's performance ins term of the number of parameters, processing speed, and mAP on the COCO dataset [37]. Table 1 summarizes the overall performance. The values in Table 1 suggest that YOLOv5-medium provides the best balance of performance between the processing speed and mAP. In contrast, YOLOv3 demonstrates the best mAP compared with the other versions, but it has the lowest frames per second (FPS), which is expected given its high number of parameters. Based on this analysis, YOLOv5-medium can be considered optimal for applications where moderate accuracy and moderate speed are acceptable, while YOLOv3 is more suitable for applications that require high accuracy with an accepted slower processing speed, which is the case in this study. Pesticide spraying is a stationary and critical process, where the robot navigates slowly between the strawberry rows to maintain stability and ensure a straight navigation line. Also, localized spraying is crucial to avoid harming the plants. This requires the robot to come to a full stop before adjusting the spraying nozzle to avoid the maximum surface of the plant. Based on that, YOLOv3 was chosen in this study since the spraying process is critical and demands high precision, which requires the highest mAP, unlike the processing speed, which is less critical because of the robot's slow movement. The YOLOv3 [38] architecture, an automatic, robust, real-time, deep learning model, was chosen to perform real-time detection of the strawberry crop in a greenhouse. YOLO stands for (You Only Look Once), and it is a family of models used for real-time object detection. As suggested by its name, YOLO refers to a single network that localizes and identifies objects in a single pass, making it rather efficient. It has been applied in many agricultural applications, e.g., [41; 42; 43]. The third version YOLOv3, which is recommended for small objects [38], combines 2 key contributions from 2 different architectures as follows: the skip connections from the ResNet architecture [17] and 3 prediction stages from Feature Pyramid Networks (FPNs) [18], resulting in the architecture shown in Figure 2.  
Compared with vanilla feedforward neural networks, the ResNet architecture uses skip connections [45] between the layers. Consequently, in the backpropagation, the weight updates are based on eliminating \(F(x)\) in order to obtain \(y=x\). This makes the ResNet