## 1 Introduction
At 1, 3, and 5 km/h, their system missed 5-9%, 6-10%, and 13-17% of the targeted weeds, respectively. The brief review of the developed systems showed that inferencing speed (_fps_) and travel velocity (\(\overleftarrow{v}\)) of a CNN-based MVS impact its detection rate (\(r_{d}\)). \(r_{d}\) is the fraction of the number of detected plants to the total number of plants and was often expressed as the recall of the CNN model [11; 24; 25]. However, a theoretical approach to predict the \(r_{d}\) of a CNN-based MVS as affected by both \(fps\) and \(\overleftarrow{v}\) is yet to be explored. Current approaches in developing a mechatronic system with CNN-based MVS involve building and testing an actual system to determine \(r_{d}\)[11; 24; 25]. However, building and testing several CNN-based MVS to determine the effect of \(\overleftarrow{v}\) and \(fps\) on \(r_{d}\) would be very tedious as the process would involve building the MVS hardware, image dataset preparation, training multiple CNN models, developing multiple software frameworks to integrate the different CNN models into the vision system, and testing the MVS. Hence,this study also proposes to use computer simulation, in addition to theoretical modeling, in predicting \(r_{d}\) as a function of the mentioned parameters, to reduce the difficulty of selecting and sizing the components of the CNN-based MVS. Computer simulations were often used to characterize the effect of design and operating parameters on the overall performance of agricultural robots for field operations. For example, [PERSON] et al. (2021) [26] demonstrated that computer simulations could be used to estimate the required sprayer spatial resolution of vision-equipped boom sprayer, as affected by boom section weeds, nozzle spray patterns, and spatial weed distribution. [PERSON] et al. (2018) [27] used computer modeling and simulation to identify potential problems of a robotic apple picking arm and developed an algorithm to improve the performance by 81%. Finally, [PERSON] et al. (2019) [28] used modeling and computer simulation to create a novel multi-perspective visual servoing technique to detect the location of occluded peppers. However, the use of simulation to quantify the effects of \(fps\), \(\overrightarrow{v}\), and camera configurations on \(r_{d}\) of an MVS remain unexplored. Furthermore, a survey of review articles showed that almost all robotic systems in agriculture employ fixed configurations and are non-scalable, resulting in less adaptability to complex agricultural environments [7; 8; 29; 30]. Thus, aside from modeling and simulation, this study also proposes a module-based design approach to enable reusability and scalability by minimizing production costs and shortening the lead time of machine development [31]. A module can be defined as a repeatable and reusable machine component that performs partial or full functions and interact with other machine components, resulting in a new machine with new overall functionalities [32]. Therefore, this study proposes theoretical and simulation approaches in predicting combinations of \(fps\), \(\overrightarrow{v}\), and camera configuration to prevent missed plant detections and aid in developing a modular CNN-based MVS.