# Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics
## 3 Mapping and Hotspot Identification
### Dynamic Map Update
### Test Site Description

The site chosen for the experiment is a pathway in Level 6 of Building 2 at the Singapore University of Technology and Design. The pathway is well-lit with natural light. A small

Figure 10: (a) Confusion matrix for YOLOv8 on the given dataset, illustrating the model’s performance across different classes such as cooler, drain, toilet, dustbin, and pot. (b) Object detection results using YOLOv8, identifying trees and drains in various urban environments. The model successfully detects and labels these objects across different lighting conditions and angles, demonstrating its robustness and accuracy in real-world scenarios. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **Precision** \\ **(\(P\))** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\(R\))** \\ \end{tabular} & \
\begin{tabular}{c} **F1 Score (\(F_{1}\))** \\ \end{tabular} &
\begin{tabular}{c} **[EMAIL_ADDRESS] (\%)** \\ \end{tabular} \\ \hline Faster RCNN & 0.56 & 0.61 & 0.58 & 0.57 \\ YOLOv5 & 0.73 & 0.60 & 0.65 & 0.69 \\ YOLOv7 & 0.70 & 0.58 & 0.63 & 0.61 \\ YOLOv8 (current model) & 0.81 & 0.71 & 0.75 & 0.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison with different YOLO versions and faster RCNN detection models for the dataset used in training the mosquito hotspots. portion of the building was taken for experimentation, as shown in Figure 11. The objects identified along the pathway were dustbins and pots. ### Hotspots Identification and Plotting on the Map

The potential breeding grounds for mosquitoes were identified during the robot's exploration phase. As depicted in Figure 12, the robot navigates through the environment, utilizing its sensors to detect and mark hotspots on the map. During its traversal, the robot leverages its integrated LiDAR, depth camera, and YOLOv8 algorithm to precisely identify locations that are likely to serve as breeding grounds for mosquitoes. In this particular scenario, the chosen hotspots include plants and dustbins. A comparative analysis (Table 6) of chemical usage in two fumigation scenarios using a precision fumigation robot was conducted, as illustrated in Figure 13. The robot, equipped

Figure 11: (**a**) Two-dimensional map of the test site and (**b**) the placement of trained objects for the robot to detect potential hotspots. Figure 12: Sequential updates of the hotspot map during the robot’s exploration. In panel (**A**), no hotspots are detected. Panel (**B**) shows the detection and mapping of “Plant-1”, marked on the map and visually confirmed in the environment. Panel (**C**) adds “Plant-2” to the map, illustrating the robot’s ability to identify and log new hotspots continuously. Finally, panel (**D**) shows the detection of a “Dustbin”, further updating the map. Each detected hotspot is localized in the mapped environment and the corresponding real-world image, demonstrating the system’s effectiveness in real-time hotspot identification and mapping. with a 10 L capacity spray gun discharging at 330 mL per minute, was tested over a 10 m \(\times\) 2 m area divided into 1 m\({}^{2}\) unit cells. The first scenario involved continuous fumigation of the entire area, with the robot moving at a speed of 0.5 m/s. The total time required to fumigate the 20 m\({}^{2}\) was 40 s, resulting in a chemical usage of 220 mL. In the second scenario, the robot discreretly fumigated identified hotspots within the same area. Three hotspots were identified, with each being sprayed for 5 s. The total time spent fumigating these hotspots was 15 s, leading to a chemical usage of 82.5 mL. The comparison demonstrates that precision hotspot fumigation significantly reduces chemical usage, achieving a savings of approximately 62.5% compared to continuous fumigation. This efficiency is due to the targeted application of the fumigant, conserving resources, and minimizing environmental impact.