## 3 Results and Discussion
### Data Analysis Using a Fine-Tuned Mask R-CNN  
The data analysis was performed using the developed Mask R-CNN with the integration of Faster R-CNN. This network model allowed the identification of pesticide residue on the leaf surface after training with well-annotated datasets (e.g., the MS COCO dataset). Moreover, fine-tuning was performed to identify and classify specific features of OTC and Kocide 3000 residue on the leaf surface. Figure 6 showcases an example of the mask R-CNN with one acquired image of citrus leaf sprayed with OTC residue (200 ppm). The developed model resulted in a high-quality segmentation mask of the area of the leaf coated with the pesticide residue. In addition, the model uses the number of pixels present in the segmented image to determine the residue coverage percentage. This model calculation of the pesticide residue coverage area is performed using the same equation as the manual pesticide coverage experiments. In this study, to evaluate the performance of the proposed model, four common metrics were assessed: Accuracy, Recall, Precision, and F1 score. The results of the metrics for both pesticides OTC and Kocide 3000 are summarized in Table 2.  
The metric related to segmentation accuracy represents the number of correctly classified data instances, which in this case is the number of correctly labeled pixels compared to the total number of data instances. The developed model showed an accuracy for OTC slightly higher than Kocide 3000, with values of 0.843 and 0.828, respectively. This is likely due to the higher sensitivity achieved while visualizing OTC images captured under UV light, compared to Kocide visualization images captured under white light. These high-accuracy results support the efficacy of the developed software to assess the pesticide residue coverage on the citrus leaves. It is worth noting that in some cases with unbalanced \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **SAM** & **SAM-Box** & **SAM-Point** \\ \hline Laboratory & 0.9894 & 0.9932 & 0.9901 \\ Field & 0.9232 & 0.9369 & 0.9295 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average segmentation accuracy from datasets collected under laboratory and field conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pesticide** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline OTC & 0.843 & 0.877 & 0.842 & 0.823 \\ Kocide 3000 & 0.828 & 0.857 & 0.821 & 0.804 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy, precision, recall, and F1 score results from testing datasets from Kocide 3000- and OTC-sprayed citrus leaves. Figure 6: Overview of the data processing by the developed Mask R-CNN using a leaf spray with OTC under laboratory conditions. The left image in the red box represents the mask generated by Mask R-CNN, the middle image in the blue box represents the originally segmented image, and the right image in the yellow box represents the superimposed image by combining the previous images. The mask generated by the Mask R-CNN provides a close estimate to the percentage of pesticide coverage through pixel-wise estimation. The superimposed image (right) conveys an overall showcase of both the area and the deposition patterns displayed by the subject. datasets, the sole use of an accuracy metric has the potential to provide misleading results. The unbalanced datasets originate from cases with significant differences between the number of pixels in one class and another. For this reason, it is important to evaluate the other metrics of Precision, Recall, and F1 score, as shown in this work. On the other hand, Precision and Recall are two metrics that work in opposition to each other. Precision provides the number of True Positive classifications against the total number of positive classifications, whereas Recall provides the number of True Positive classifications against the sum of True Positives and False Negatives. Ideally, both Precision and Recall should be equal to 1, as the ideal number of misclassifications should be 0.