## 5 Experimental Evaluation
### Hardware Setup  
The proposed frameworks are implemented in the tobacco fields with a four-wheeled mobile robot platform. The robot has a track width and wheelbase of 1 and 1.3 m, respectively. In order to protect tobacco plants from the robot, the ground clearance of the platform was carefully chosen as 0.9 m. Moreover, the height of the robot's platform can be adjusted anywhere between 0.9 and 0.4 m depending on different crops. In order to keep robot design and control simple, a differential drive scheme was chosen with two driving wheels (front) and two passive wheels (rear). The robot is equipped with two DC motors connected to motor controllers for steering and driving the robot along the straight crop rows. Two separate RGB cameras are mounted on the robot: One is used for the crop row detection (for navigation), and the other is used for crop/weed detection (for spraying). The camera for row structure detection is mounted at the front with its face towards the ground and a horizon at an angle of 35\({}^{\circ}\) with the horizontal axis, covering three rows simultaneously. The camera for crop and weed detection is mounted at the front of the robot and oriented facing downwards to the ground at a fixed distance of 1.8 m from nozzles on the boom. The distance between the crop and weed detection camera and the boom is kept at the maximum in order to provide the desired time delay between detection and position estimation of the crop plant and the spray application process on every corresponding grid cell. The vision-based detection system is coupled with spraying equipment and other sensing modules, thereby making a complete precision agricultural robotic spraying system. A 12 V DC diaphragm pump is used to pressurize the fluid system. An electronic pressure regulated valve maintains a constant line pressure when different nozzles on the boom are switched ON and OFF based on feedback from the vision system and other sensing modules. The outflow line from the pump is divided into a bypass line that diverts excess flow back to the tank and a boom line onto which the nozzles are mounted. Two rotary incremental encoders (with resolutions of 1000 pulses per revolution) connected to the  
Figure 7: Modified YOLOv5 detection pipeline. embedded controller are mounted on the front wheels' axles to measure the rotation (and thereby speed) of the wheels. The incremental encoders and a GPS module facilitates the robot in determining its position and heading direction for navigation. Moreover, the optical data acquired via cameras are synchronized to the robot's position through incremental encoders and GPS module. The robot used ROS (Robot Operating System) as the middleware software framework. The cameras were connected to a computer possessing an Intel Core E5-1620, a 3.50 GHz processor, 32 GB RAM, and an 8 GB NVIDIA GTX 1650 Ti GPU for processing the images. Moreover, Microsoft Visual Studio and Python were used for program development. The developed agricultural robot sprayer and its overall functional block diagram is shown in Figures 8 and 9, respectively. a total of 5600 images (4900 tobacco and 700 weeds), whereas the testing set comprised 2400 images (2100 tobacco and 300 weeds). In the implementation phase, the models are trained using down-sampled images (with a resolution of \(640\times 480\)). A learning rate is initialized as 0.0002 for the training, Google's TensorFlow API is utilized for implementation purposes. Batch sizes of 1 and 10 k epochs are used for training the models. Table 1 lists the hyper-parameters and their corresponding losses (against the epochs) for both models. It can be observed from Table 1 that for obtaining better results with Faster R-CNN-based vision model, the learning rate is kept the same, whereas the other hyper-parameters did change. With an increase in the number of epochs, total loss is reduced.