Moreover, it was observed that fine-tuning outperformed feature extraction process. [PERSON] et al. [58] detected weeds in soybean crops using ConVNets and SVM classifiers. ConVNets was able to achieve higher accuracy of more than 97% in weed detection. [PERSON] et al. [59] used deep learning algorithms for detecting multiple weed species in Bermuda grass. The study reported that VGGNet performed well with an F1-score of over 0.95 than compared to GoogleNet. Moreover, F1-scores of over 0.99 were reported for detecting weeds via DetectNet. The authors, based on attained results, concluded the effectiveness of deep convolutional neural networks in the weed detection problem. In another study, [PERSON] et al. [60] evaluated three CNNs--DetectNet, VGGNet, and GoogLeNet--for the detection of weeds in strawberry fields. It was observed that the image classification DetectNet model produced the best results for image-based remote sensing of weeds. [PERSON] et al. [61] used Faster R-CNN for the detection of weeds in Barley crops using several feature extractors. In the study, mean Average Precision (mAP) with Inception-ResNet-V2 was found better than the mAP for other networks. Moreover, an inference time of 0.38 s per image was also reported. [PERSON] et al. in [62] presented an improved version of the Faster R-CNN vision system for the identification of maize seedlings in tough field environments. The images were taken with a camera at an angle ranging from 0 to 90 degrees. The results reported detection accuracy of 97.71%. In the study performed by [63], the authors reported F1-scores of 88%, 94%, and 94% for SVM, YOLOv3, and Mask R-CNN for detecting weeds in lettuce crops, respectively. The work reported by [PERSON] et al. [64] used YOLOv4-based vision system for detecting apple flowers. The model based on CSPDarkNet-53 framework was simplified with a channel pruning algorithm for detecting the target object in real time. They reported achieving a mAP of 97.31% at a detection speed of 72.33 fps. Despite the impressive accomplishments in deep learning-based object detection, the performance of these algorithms has yet to be evaluated in the realm of tobacco plants and weeds detection; for instance, the use of region-based methods such as Faster R-CNN or one stage detectors such as YOLOv5. Moreover, published reports also lack experimental validation in actual field environments. The aim of this study includes the replacement of conventional broadcast spraying methods in tobacco fields with a site-specific (drop-on-demand) spraying system. The proposed method detects and classifies tobacco plants and weed automatically, determines their position, i.e., their location in the crop rows, and finally performs agrochemical spray on the detected targets. This paper focuses on automatic vision-based tobacco plant detection that is considered a vital part of the precision spraying system. The basic frameworks of two off-the-shelf deep-learning algorithms--Faster R-CNN and YOLOv5--are employed for detection and classification models. The robustness and ability of the models are enhanced by fine-tuning detection of tobacco plants in challenging field conditions. Both detection models are tested on a vision-guided mobile robot platform in real tobacco fields. A comparative study is also carried out between both frameworks in terms of robustness, accuracy, and inference/computational speed. The Faster R-CNN-based vision-based model demonstrated higher accuracy but lower real-time detection speed, whereas the YOLOv5-based model produced slightly lower accuracy but higher real-time detection speed. Therefore, YOLOv5-based vision model, based on its performance, is considered best suited for real-time tobacco plant and weed detection. The main contributions of this study are summarized as follows:  
1.