Automated seed counting using image processing and deep learning.

Abstract

Introduction Accurate 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. Methods This 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. Results The 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. Discussion Both 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.

Code and data availability

The paper's seed image dataset, IP/DL code, and trained YOLOv5 model are not publicly deposited; the Data Availability Statement only offers raw data on request. The only URL present (labelImg) is a generic third-party annotation tool, not a paper-specific asset.

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