Spray Quality Assessment on Water-Sensitive Paper Comparing AI and Classical Computer Vision Methods

Inês Simões (), Armando Jorge Sousa (), André Baltazar and Filipe Santos
Additional contact information
Inês Simões: FEUP—Faculdade de Engenharia da Universidade do Porto, 4200-465 Porto, Portugal
Armando Jorge Sousa: FEUP—Faculdade de Engenharia da Universidade do Porto, 4200-465 Porto, Portugal
André Baltazar: INESC TEC—INESC Technology and Science, 4200-465 Porto, Portugal
Filipe Santos: INESC TEC—INESC Technology and Science, 4200-465 Porto, Portugal

Agriculture, 2025, vol. 15, issue 3, 1-25

Abstract: Precision agriculture seeks to optimize crop yields while minimizing resource use. A key challenge is achieving uniform pesticide spraying to prevent crop damage and environmental contamination. Water-sensitive paper (WSP) is a common tool used for assessing spray quality, as it visually registers droplet impacts through color change. This work introduces a smartphone-based solution for capturing WSP images within vegetation, offering a tool for farmers to assess spray quality in real-world conditions. To achieve this, two approaches were explored: classical computer vision techniques and machine learning (ML) models (YOLOv8, Mask-RCNN, and Cellpose). Addressing the challenges of limited real-world data and the complexity of manual annotation, a programmatically generated synthetic dataset was employed to enable sim-to-real transfer learning. For the task of WSP segmentation within vegetation, YOLOv8 achieved an average Intersection over Union of 97.76%. In the droplet detection task, which involves identifying individual droplets on WSP, Cellpose achieved the highest precision of 96.18%, in the presence of overlapping droplets. While classical computer vision techniques provided a reliable baseline, they struggled with complex cases. Additionally, ML models, particularly Cellpose, demonstrated accurate droplet detection even without fine-tuning.

Keywords: precision agriculture; spray quality assessment; water-sensitive paper; computer vision; YOLOv8; Mask-RCNN; Cellpose; synthetic dataset (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/3/261/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/3/261/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:3:p:261-:d:1576944

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().