###### Abstract
MobileNet is a lightweight deep neural network architecture designed for low-computation devices and embedded vision applications. Since then, various lightweight models such as MobileNet-V1 [17], MobileNet-V2 [18], MobileNet-V3 [19], and MobileDet [20] have been developed. In addition to the architectural designed for edge devices, an application-specific integrated circuit (ASIC) called the Tensor Processing Unit (TPU) has been developed by Google as an Artificial Intelligence (AI) accelerator for neural networks. For low-power devices, such as IoT edge devices, it provides high-performance inference for Machine Learning (ML) and accelerates ML inference on these devices. For example, advanced mobile image processing models such as MobileNet-V2 can run at approximately 400 frames per second with minimal power consumption [21]. The Single Shot Multibox Detector (SSD) [22] is designed for real-time object detection. The SSD is a state-of-the-art single-shot detection method that speeds up the process of detecting the target object in the images. The SSD design is based on a classification model such as the MobileNet architecture without fully connected layers called the backbone. To detect objects in the images, additional convolutional layers are added to the backbone. In [PERSON] et al. [23], SSD were trained using MobileNet-V1 and the Inception model as a backbone to detect mid- and early-stage grapes. To test the temporal accuracy of the model, the models were transmitted to the TPU Edge device. The SSD with MobileNet-V1 outperformed the SSD with Inception model in terms of accuracy and time inference on the Edge TPU device. Robotics can be used in agriculture to enrich fruit harvesting, weeding, pruning, plowing, irrigation, monitoring, plowing, spraying, and sheep shearing. Agricultural land has a very complex environment and one of the challenges of using robots in agriculture is navigating the robots in this complex environment. [PERSON] et al. [24] developed a low-cost,energy-efficient local motion planner based on RGB-D images for autonomous navigation of robots in vineyards. The disparity map and its depth representation was employed to create proportional control for the robot platform. In case the first algorithm fails, a deep learning algorithm was used to take over the control of the machine. In [PERSON] et al. (2019), SSD MobileNet-V1 with different hyperparameters, SSD MobileNet-V2, Pooling Pyramid Network (PPN) MobileNet-V1, SSDLite MobileNet-V2, SSD Inception-V2, Tiny YOLO-V3 were trained to detect the trunk of vines in two vineyards in Portugal. Fine trunk detection can help create an accurate map of the vineyard. The trained models were transferred to two Edge devices, including Google's USB Accelerator and NVIDIA's Jetson Nano. Google's USB Accelerator outperformed the Jetson Nano in terms of time inference. [PERSON] et al. (2019) trained the SSD model using MobileNet-V1 and MobileNet-V2 as backbones to detect the trunk in a vine. The model was deployed on an edge device (Raspberry Pi with Google's USB Accelerator) in order to investigate accuracy and time inference. SSD with MobileDet Edge TPU (Kal  
In the next step, the images were manually labeled in the publicly available Pascal format VOC. To increase the diversity and robustness of the VineSet, the augmentation methods were used in [26] and the images were expanded to 9481 images. For the following two reasons, the original data were used without augmentation:  
* The first and most important point was that the dataset was augmented before splitting the data into train and test. When performing the splits, the same image could appear in different splits with small changes in angle or brightness, which would have made the validation and testing step less effective (see Section 3.4). * Second, our computational power was limited. Training the DL models requires a system with very high computational power (GPU) and memory. The more input data, the more training time and the more memory required.