## 7 Conclusions  
Intelligent precision agriculture robot sprayers for agrochemical application must be robust enough to distinguish crops from weeds to perform targeted spraying to reduce the usage of agrochemicals. In this paper, two different CNN-based approaches, namely, Faster R-CNN and YOLOv5, are explored in order to develop a vision-based framework for the detection and classification of tobacco crop and weeds in the actual fields. Both frameworks are first trained and then tested on a self-developed tobacco plants and weeds dataset, TobSet. The dataset comprises 7000 images of tobacco plants and 1000 images of bare soil and weeds taken manually with digital cameras periodically over 2 months. The Faster R-CNN-based vision framework demonstrated higher accuracy and robustness, whereas the YOLOv5-based vision framework demonstrated lower inference time. Experimental implementation is conducted in the tobacco fields with a four-wheeled mobile robot sprayer with a computer possessing a GPU. Classification accuracies of 98% and 94% and frame rates of 10 and 16 fps were recorded for Faster R-CNN and YOLOv5-based models, respectively. Moreover, the precise smart application of pesticides with the proposed system offered 52% reduction in pesticide usage by pinpointing the targets, i.e., tobacco plants.  
Faster R-CNN produces higher accuracy but lower fps on computers (especially without GPUs); high computational cost of training makes it challenging for real-time  
Figure 14: Real-time YOLOv5 detection of tobacco crop and weeds in scenarios with **(a)** low intra-row plant distance, and **(b)** high intra-row plant distance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Faster R-CNN** & **YOLOv5** \\ \hline Inference time (ms) & \(98.5\pm 5\) & \(62\pm 5\) \\ Frames per second (fps) & \(10.15\) & \(16.12\) \\ mAP & \(0.94\) & \(0.91\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Inference results of the models in real-time.  
applications. TobSet demonstrated true assessment of the deep-learning algorithms as it comprises real field images with challenging scenarios possessing different factors of variation, such as dense weed patches, lightening variation, color similarity with weeds, color variation of tobacco plant at different growth stages, and varying growth stages. The classification results of both approaches in real time were slightly lower than the prediction results obtained on the dataset due to higher sunlight intensities. Intended future studies include real-time tobacco plant segmentation for finding canopy size and the desired application flowrate of spray for each tobacco plant.  
Conceptualization, M.S.A., M.T. and M.U.K.; methodology, M.S.A., M.A. and M.T.; software, M.S.A., M.A. and F.E.N.; validation, A.G., B.S., W.S. and M.U.K.; formal analysis, M.T.K., B.S. and W.S.; resources, M.T. and M.T.K.; data curation, M.S.A., M.A. and F.E.N.; writing--original draft preparation, M.S.A. and M.A.; writing--review and editing, M.U.K., M.T. and A.G.; visualization, M.U.K., A.G. and B.S.; supervision, M.T., B.S. and M.T.K.; project administration, M.T., B.S. and M.T.K.; funding acquisition, B.S. All authors have read and agreed to the published version of the manuscript.  
This study received funding from King Saud University, Saudi Arabia, through researcher's supporting project number (RSP-2021/145). The APCs were funded by King Saud University, Saudi Arabia, through researcher's supporting project number (RSP-2021/145).  
Not applicable.  
The dataset used in this study is publicly available on GitHub at [[https://github.com/mshahabalam/TobSet](https://github.com/mshahabalam/TobSet)]([https://github.com/mshahabalam/TobSet](https://github.com/mshahabalam/TobSet)) (accessed on 11 October 2021).  
The authors extend their appreciation to King Saud University, Saudi Arabia, for funding this study through researcher's supporting project number (RSP-2021/145).  
The authors declare no conflict of interest.