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Automated seed counting using image processing and deep learning
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OA
AI
DOI：10.3389/fpls.2025.1659781.png)
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Abstract
En 中文
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.
Keywords:
seed counting
computer vision
mobile application
image processing
deep learning
Journal
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14.7W