Authors:
Inês Simões
1
;
2
;
André Baltazar
2
;
Armando Sousa
1
;
2
and
Filipe Santos
2
Affiliations:
1
FEUP - Faculdade de Engenharia da Universidade do Porto, Universidade do Porto, R. Dr. Roberto Frias, Porto, Portugal
;
2
INESC TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, R. Dr. Roberto Frias, Porto, Portugal
Keyword(s):
Precision Agriculture, Water-Sensitive Spray, Classical Computer Vision, Spray Quality Assessment, Instance Segmentation.
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% fo
r 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.
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