# Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics
## 3 Mapping and Hotspot Identification
### Dynamic Map Update
\begin{table}
**Aspect** & **Ref. [(29)]** & **Ref. [(30)]** & **Ref. [(31)]** & **Ref. [(32)]** & **Proposed Robot** \\ \hline Robot developed & & & & & \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{AI Integration} & \multirow{2}{*}{SVM classifier for leaf density} & Improved YOLOv5 & \multirow{2}{*}{Novel scene} & Deep learning model & YOLOv8 for \\  & & & model with & representation and & with CBAM and & dynamic hotspot \\  & & & mechanisms & motion planning & BiFPN & localization \\ Urban Navigation & No & No & No & No & Yes \\ (Complexx & (focused on & (focused on & (designed for & (agricultural fields) & (agricultural fields) \\ Environment) & agricultural fields) & agricultural fields) & early-stage crops) & early-stage crops) & (specifically \\  & & & Yes, real-time & & \\ Real-Time & Yes, but limited to & performance with & Yes, sub-centimeter & Yes, real-time & Yes, with real-time \\ Operation & agricultural fields & 30 ms/frame & precision in spraying & detection and spraying & mapping and \\  & & detection speed & & spraying & fumigation \\ Mapping & & No & No & Limited & No & Dynamic mapping \\ Capabilities & & High precision in & & High precision for & \\ Precision & \begin{tabular}{c} High precision in \\ spraying based on \\ leaf density \\ \end{tabular} & \begin{tabular}{c} High precision for \\ corn and weed \\ identification \\ \end{tabular} & \
\begin{tabular}{c} High precision for \\ volume \\ spraying \\ \end{tabular} &
\begin{tabular}{c} High precision for \\ mosquito hotspots \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison between the fumigation operation of existing precision spray robots and proposed precision fumigation robot. fumigating motion. Using advanced machine learning techniques and identifying the environment, a decision can be made to opt for the required strategy. * Navigating multiple floors in a building: The proposed challenge can be solved by integrating the robot's control with the building's management system, such as access to calling lifts and doors. However, this requires planning from the initial stage to make the building's infrastructure robot-friendly. ### Limitations

The limitations of the proposed work with the integration of YOLOv8 in the precision fumigation robot are as follows:

* The trained model may underperform in scenarios significantly different from its training environment, restricting its effectiveness in unfamiliar urban landscapes. * YOLOv8 needs robust computational resources, which could limit the deployment of the robot in settings with limited processing capabilities. * Optimizing for real-time performance compromises the detection accuracy, which is critical for precise localization of mosquito breeding sites. * The model's effectiveness decreases with smaller objects, which could be crucial in identifying less conspicuous breeding grounds. * Vibrations during locomotion affect detection accuracy, leading to blurred images being sent for object recognition. ## 5 Conclusions

The development of a fumigation robot utilizing LiDAR data, depth camera data with YOLOv8 for object recognition, and IMU data represents a significant advancement in targeting mosquito breeding hotspots. Fusing these sensor inputs enables precise localization and comprehensive data collection, ensuring that potential breeding sites are accurately identified and treated. This technology enhances the efficiency of fumigation efforts and significantly reduces human exposure to harmful chemicals, thereby improving safety and operational outcomes. Integrating advanced sensors and algorithms within this robotic system marks a critical step forward in urban pest management, offering a robust solution to a pervasive public health challenge. Further research and development can optimize these systems, making them more adaptable to diverse environments and improving their efficacy in real-world applications. Conceptualization, S.J. and P.K.C. ; methodology, S.J. and P.K.C. ; software, S.J.