## 4 Conclusions  
In the present work, a reliable and non-destructive method for detecting and assessing the pesticide residue coverage of the surface of citrus leaves was developed. This software provides an affordable alternative for citrus growers to use in field conditions to guide their current pesticidal application cycles. The 3D-printed sample compartment prototype fabricated for this work is robust, inexpensive, and portable, enabling a more controlled environment for image data acquisition under field conditions. Both studied pesticides, Kocide 3000 and OTC, exhibited a visible pattern of residue deposition on the leaves, allowing the surface image analysis to determine their coverage. The residue image analysis showed a linearly proportional relationship between the concentration of applied pesticide and the area of coverage. This property is desirable in the development of an imaging analysis software assisted by a lightweight machine learning system. Furthermore, the SAM segmentation model with the developed model for fine-tuning a pre-trained Mask R-CNN showed excellent performance metrics to generate segmentation masks of pesticide residue coverage. These findings support the undeniable advantages of integrating recent technological advancements to foster more efficient, affordable, and sustainable agricultural practices. The developed platform has the potential to directly benefit citrus growers, providing a suitable and equitable alternative for pesticide residue coverage analysis. This technology can mitigate the socio-economic challenges faced by small-scale citrus growers, addressing pesticide residue coverage in real-time with an affordable tool. The on-site pesticide residue coverage information may guide the decision-making-process-related crop management and mitigate the excessive overuse of pesticide. Moreover, in regions with limited technological infrastructure this tool may bridge the gap between growers with large and small operations, with cost savings and reduced financial constraints. In the future, the current limitations of this methodology and its general applicability to other crops will be addressed. Some of these challenges are improving accuracy, F1 scores, sample size, and data augmentation strategies. For this reason, further studies will incorporate larger numbers of samples and samples subjected to rainfall events to better assess the residue persistence under harsher environmental conditions. In addition, this work will serve as a foundational study to explore the development of a larger software that could encompass other pesticides and crops relevant to the agricultural industry. Ultimately, more efforts are underway to improve the model's accuracy and F1 score with the application of standard data augmentation technique to improve performance, especially during the future end-user testing. In addition, increasing to a larger annotated dataset could overcome some of the challenges associated with leaf defects causing false positives. These improvements will foster the software development process to proceed into later stages with user interface design and end-users testing feedback. Conceptualization, A.B. and S.S.; methodology, A.B., G.D., and E.D. ; software, A.B. ; validation, A.B. ; formal analysis, A.B. ; investigation, A.B., G.D., and E.D. ; resources, S.S.; data curation, A.B. ; writing--original draft preparation, A.B. ; writing--review and editing, E.D., G.D., C.C., and S.S.; supervision, S.S.; project administration, S.S. and C.C. ; funding acquisition, S.S. and C.C. All authors have read and agreed to the published version of the manuscript. This research was supported by an intramural research program of the U.S. Department of Agriculture, National Institute of Agriculture, project #2024-67022-41788. The findings and conclusions in this preliminary publication have not been formally disseminated by the U.S. Department of Agriculture and should not be construed to represent any agency determination or policy. Not applicable.