Omslagsbillede

Automated seed counting using image processing and deep learning

Guardado en:
Udgivet i:Frontiers in Plant Science vol. 16 (Aug 2025), p. 1659781-1659797
Hovedforfatter: Zu, Qiuyu
Andre forfattere: Liu, Teng, Zhu, Wenpeng, Pan, Yan, Wang, Jinxu, Song, Xinru, Yu, Jialin, Dang, Shu, Yu, Xiaoming, Zhang, Zhenyu
Udgivet:
Frontiers Media SA
Fag:
United States > US
Kentucky
Accuracy
Deep learning
Plant breeding
Applications programs
Agricultural research
Weed control
Corn
Mobile computing
Environmental conditions
Crops
Image processing
Computer vision
Automation
Efficiency
Seeds
Modernization
Lighting
Labor
Methods
Human error
Environmental
Online adgang:Citation/Abstract
Full Text
Full Text - PDF
Tags: Tilføj Tag
Ingen Tags, Vær først til at tagge denne postø!
Resumen:IntroductionAccurate seed counting is an essential task in agricultural research and farming, supporting activities such as crop breeding, yield prediction, and weed management. Traditional manual seed counting, while accurate, is time-consuming, labor-intensive, and prone to human error, particularly for large quantities of micro-sized seeds.MethodsThis study developed two automated computer vision approaches integrated into a mobile application (app) for seed counting: one utilizing image processing (IP) and the other based on deep learning (DL). These methods aim to address the limitations of traditional manual counting by providing automated, efficient alternatives.ResultsThe IP-based method demonstrated high accuracy comparable to manual counting and offered substantial time savings. However, its reliance on controlled environmental conditions, such as uniform lighting, limits its versatility for field apps. The DL-based method excelled in speed and scalability, processing counts in as little as 0.33 seconds per image, but its accuracy was inconsistent for visually complex or densely clustered seeds.DiscussionBoth automated methods significantly enhance the efficiency of seed counting, providing a practical and accessible solution for various agricultural contexts. The integration of these methods into a mobile app streamlines seed counting for laboratory research, field studies, seed production, and breeding trials, offering a transformative approach to modernizing seed counting practices while reducing time and labor requirements.
ISSN:1664-462X
DOI:10.3389/fpls.2025.1659781
Fuente:Agriculture Science Database