## 1 Introduction
Consequently, they are not suitable for real-time field detection applications. Delving further into the realm of real-time detection, [PERSON] et al. introduced the YOLO (You Only Look Once) architecture [19]. Unlike traditional methods that predict a proposal and subsequently refine it, YOLO directly predicts the final detection by dividing the image into grids and performing a prediction for each grid cell. This makes the YOLO network the most representative network with real-time capabilities [20]. Currently, there are 10 versions that have improved upon the performance of the original YOLO. YOLOv3 was the first YOLO model to be widely adopted in real-time applications. It demonstrates remarkable results in detecting the smallest objects, thanks to its deep architecture, which incorporates three size prediction layers. [PERSON] et al. [21] proposed a real-time weed detection system for green onion crops using YOLOv3. The system was able to reach an AP of 93.81% and an F1 score of 0.94 on a five-minute UAV video with a resolution of 864 \(\times\) 688 using a high-performance computer. In another study, [PERSON] et al. [22] used a circular bounding box with the YOLOv3 model. This resulted in a mean AP of 0.96 when using an image of 3648 \(\times\) 2056 pixels at a speed detection rate of 0.054 s.  
Regarding strawberry-related applications, [PERSON] et al. [23] applied the YOLOv4 network for strawberry maturity detection and localization for robotic harvesting in RGB images. Their approach employed a two-stage deep learning methodology utilizing YOLOv4 and YOLOv4-tiny models to identify mature strawberries and estimate their centers from both RGB and depth images. They reached an AP of 91.73% with a processing speed of 55.19 ms per image for maturity detection and a mean AP of 86.45% with a 4.16 ms processing time per image for strawberry localization. [PERSON] et al. [24] proposed a method for the rapid and accurate identification of strawberry fruit in greenhouses using an improved YOLOv5s approach. Their method, called YOLO-SR (YOLO-Strawberry Ripeness), replaced the C3 module with a lightweight multi-path aggregation network based on Channel Shuffle, enhancing the feature extraction capability. This method achieved a mAP of 94.3% and an F1 score of 93.7%. However, running these algorithms in a practical setting using small robots is very difficult because the YOLO network needs a strong GPU (Graphics Processing Unit) with over 4 Gigabytes of memory, which most electronic boards cannot handle [20]. As a solution, researchers have developed a lightweight version of the YOLO network, introducing the term YOLO-tiny. In their study, [PERSON] et al. [25] designed and implemented a lightweight deep neural network (RTSD-Net) for real-time strawberry detection on embedded devices, such as Jetson Nano. By reducing the number of convolutional layers and the cross-stage partial network of a YOLOv4-tiny architecture, and by using the TensorRT method to speed up the model, they achieved a mean AP of 82.44% on an aerial strawberry dataset, and the processing speed was 25.2 frames per second (FPS). These results were achieved using a dataset comprising only three types of objects, with a uniform image background, which may not be representative of all types of scenarios. Additionally, the network architecture of the YOLO-tiny model has fewer layers and parameters, allowing for quicker performance but increasing the risk of information loss in complex environments [26]. To the authors' best knowledge, there have been no previous studies on the use of YOLO models for strawberry plant detection in complex environments for pesticide spraying application. Consequently, this paper explores the possibility of using the YOLO model for detecting strawberry plants in such types of applications.