Abstract
Over recent decades, precision agriculture has revolutionized farming by optimizing crop yields and reducing resource use through targeted applications. Existing portable spray quality assessors lack precision, especially in detecting overlapping droplets on water-sensitive paper. This proposal aims to develop a smartphone application that uses the integrated camera to assess spray quality. Two approaches were implemented for segmentation and evaluation of both the water-sensitive paper and the individual droplets: classical computer vision techniques and a pre-trained YOLOv8 deep learning model. Due to the labor-intensive nature of annotating real datasets, a synthetic dataset was created for model training through sim-to-real transfer. Results show YOLOv8 achieves commendable metrics and efficient processing times but struggles with low image resolution and small droplet sizes, scoring an average Intersection over Union of 97.76% for water-sensitive spray segmentation and 60.77% for droplet segmentation. Classical computer vision techniques demonstrate high precision but lower recall with a precision of 36.64% for water-sensitive paper and 90.85% for droplets. This study highlights the potential of advanced computer vision and deep learning in enhancing spray quality assessors, emphasizing the need for ongoing refinement to improve precision agriculture tools. © 2024 by SCITEPRESS-Science and Technology Publications, Lda.

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.