# Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics
## 3 Mapping and Hotspot Identification
### Dynamic Map Update
The formulae for each parameter are given below. \[Precision\ (P)=\frac{t_{p}}{t_{p}+f_{p}} \tag{1}\]

\[Recall\ (R)=\frac{t_{p}}{t_{p}+f_{n}} \tag{2}\]

\[F_{measure}(F_{1})=\frac{2\times precision\times recall}{precision+recall} \tag{3}\]

Figure 8: Fusion of YOLOv8, LiDAR frame, and depth camera frame for precision identification and localization of fumigation hotspot. Figure 7: The process flow of updating hotspot markers on the real-time map. \[mAP=\frac{1}{n}\sum_{i=1}^{n}AP_{i} \tag{4}\]

It can be seen in Table 4 that the model trained has a high precision value of 0.81, which means there are fewer chances of false positives. Since the proposed work is not strict in identifying the nature of the hotspot, a slightly low recall value is good enough, which is evident from the \(F_{1}\) score. The detection for this current research was conducted on dustbins and pots. Though the dustbin has a \(mAP\) value lower than that of other classes, it gave us a fair performance during our experiment. A performance comparison test was performed to check the effectiveness of the proposed model compared to other detection models. The proposed system is more particular about the model's input as video; thus, the other models are eliminated, and a performance evaluation test is performed among the YOLO series of models. Each of these models was trained with the same number of datasets. Figure 9 shows the overall result of the trained model. Figure 10 shows the confusion matrix and detection results from the YOLOv8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Precision** & **Recall** & **F\({}_{1}\) Score** & **[EMAIL_ADDRESS] (\%)** \\ \hline All & 0.81 & 0.69 & 0.74 & 0.74 \\ Cooler & 0.82 & 0.84 & 0.83 & 0.93 \\ Drain & 0.95 & 0.91 & 0.93 & 0.94 \\ Toilet & 0.87 & 0.88 & 0.88 & 0.94 \\ Dustbin & 0.75 & 0.51 & 0.61 & 0.62 \\ Pot & 0.99 & 1 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics of the YOLOv8 detection model used in the proposed precision fumigation robot. Figure 9: Training and validation metrics for YOLOv8, showing the progression of losses (box, classification, and objectness) and performance metrics (_precision, recall, [EMAIL_ADDRESS],_ and _[EMAIL_ADDRESS]:0.95_) over 100 epochs. The graphs illustrate the model’s improvement in accuracy and reduction in errors as training progresses, with consistent convergence observed in both training and validation phases. YOLOv8 achieves a _Precision_ of 0.81, a _Recall_ of 0.71, \(F_{1}\)_score_ of 0.75, and a \(mAP\) of 0.74, outperforming the other models across all metrics. Specifically, compared to Faster RCNN (\(P\): 0.564, \(R\): 0.61, \(F_{1}\): 0.58, \(mAP\): 0.57), YOLOv8 shows a 43.7% improvement in _Precision_, a 15% increase in _Recall_, a 27.3% boost in \(F_{1}\)_score_, and a 29.8% enhancement in \(mAP\). When compared to YOLOv5 (\(P\): 0.73, \(R\): 0.60, \(F_{1}\): 0.65, \(mAP\): 0.69), YOLOv8 demonstrates an 11% improvement in _Precision_, an 18.3% increase in _Recall_, a 15.4% boost in \(F_{1}\)_score_, and a 7.2% enhancement in \(mAP\). Similarly, compared to YOLOv7 (\(P\): 0.70, \(R\): 0.58, \(F_{1}\): 0.63, \(mAP\): 0.61), YOLOv8 shows a 15.7% improvement in _Precision_, a 22.4% increase in _Recall_, a 19% boost in \(F_{1}\)_score_, and a 21.3% enhancement in \(mAP\). These quantitative improvements indicate that YOLOv8 is more reliable, offering higher precision and recall rates, translating to fewer _fp_ and _fn_. The efficiency gains in terms of \(F_{1}\)_score_ and \(mAP\) also suggest that YOLOv8 provides a more balanced and accurate detection. It can be inferred from Table 5 that the selected YOLO model has good precision compared to other models, which means it is the best at avoiding _fp_. The \(mAP\) score (0.74) for YOLOv8 is better than that of the other models, which means it is better at localizing objects. Considering the above points, YOLOv8 was chosen as the object detection model.