## 1 Introduction  
The increasing cost and decreasing availability of agricultural labor [1, 2] and the need for sustainable farming methods [3, 4, 5] led to the development of robots for agricultural field operations. However, despite the success of robots in industrial applications, agricultural robots for field operations remain primarily in the development stage due to the complex characteristics of the farming environment, high cost of development, and high durability, functionality, and reliability requirements [6, 7, 8]. As a potential solution to these challenges, field robots with computer vision have been increasing due to the large amount of information that can be extracted from an agricultural scene [9]. Real-time machine vision systems (MVS) are often used to recognize, classify and localize plants accurately for precision spraying [10; 11], mechanical weathering [12], solid fertilizer application [13], and harvesting [14; 15]. However, using traditional image processing techniques, early machine vision implementations for field operations were difficult due to the vast number of features needed to model and differentiate plant species [13] and work at various farm scenarios [12]. Recently, developments in deep learning allowed Convolutional Neural Networks (CNN) to be used for accurate plant species detection and segmentation [16; 17]. However, despite high classification and detection performance, the large computational power requirement of CNN limits its application in real-time operations [18]. As a result, most CNN applications in agriculture were primarily employed in non-real-time scenarios, excluding inferencing speeds in the evaluated parameters among related studies. For example, a study that surveyed CNN-based weed detection and plant species classification reported 86-97% and 48-99% precisions, respectively, but data on inferencing speeds were unreported [19]. Similarly, research on fruit classification and recognition using CNN showed 77-99% precision, but inferencing speeds were also excluded in the measured parameters [19; 20; 21]. Few studies have evaluated the real-time performance of CNNs for agricultural applications. For example, in a study by [PERSON] et al. (2019) [22] on detecting different species of weeds, the real-time performance of ResNet-50 in an NVIDIA Jetson TX2 was only 5.5 _fps_ at 95.1% precision. Optimizing their TensorFlow model using TensorRT increased the inferencing speed to 18.7 _fps_. The study of [PERSON] et al. (2019) [23] using a custom CNN architecture based on U-Net, MobileNets, DenseNet, and ResNet in a Raspberry Pi 3B+ resulted in only 60.2% precision at 10.0 _fps_. Finally, [PERSON] et al. (2019) [11] developed a mobile robotic sprayer that used YOLOv3 running on an NVIDIA 1070 TI at 22 _fps_ and NVIDIA Jetson TX2 at 2.4 _fps_ for real-time crop and weed detection and spraying. The vision system with 1070 TI had 85% precision, while the TX2 vision system had 77% precision. Furthermore, the system with TX2 missed 43% of the plants because of its slow inferencing speed. Conversely, the CNN-based sprayer with the faster 1070 TI, resulting in higher inferencing speed, only missed 8% of the plants. However, the travel velocities of surveyed systems were often unevaluated despite operating in real-time. Additionally, theoretical approaches to quantify and account for the effect of travel velocity on the capability of the vision system to sufficiently capture discrete data and effectively represent a continuous field scenario were often not included in the design. Other studies, on the contrary, evaluated the effect of travel velocity on CNN detection performance, but the impact of inferencing speed remained unevaluated. For instance, in the study of [PERSON] et al. (2021) [24], the deep learning-based variable rate agrochemical spraying system for targeted weeds control in strawberry crops equipped with a 1080 TI showed increasing missed detections, as travel velocity increases regardless of CNN architecture (VGG-16, GoogleNet or AlexNet).