A method for counting corn tassel branches based on digital images and semi-supervised learning

By using the YOLOv5-C3CA network model and semi-supervised learning methods, the problem of high time consumption and high cost in counting maize tassel branches was solved, achieving efficient and accurate maize tassel branch identification, and providing basic data for maize breeding and precision agriculture.

CN116343044BActive Publication Date: 2026-01-27CHINA AGRI UNIV
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Patent Information

Application Number
CN202310329989.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-03-30
Publication Date
2026-01-27
Estimated Expiration
2043-03-30

AI Technical Summary

Technical Problem

Existing methods for counting the branches of maize tassels are time-consuming, costly, and cannot be scaled up. Traditional manual counting is inefficient and subject to subjective factors.

Method used

A semi-supervised learning method based on the YOLOv5-C3CA network model was adopted to identify maize tassels and extract the number of branches through digital images. The semi-supervised learning method reduces the workload of data labeling and improves the recognition accuracy.

Benefits of technology

It achieves accurate identification of maize tassels and branches, reduces data annotation costs, ensures the detection performance of the model, and is suitable for maize breeding and precision agriculture.

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Abstract

A corn tassel branch counting method based on digital images and semi-supervised learning belongs to the field of plant phenotype measurement. The method is: the construction of a corn tassel branch identification model YOLOv5-C3CA: a new network model YOLOv5-C3CA is constructed based on YOLOv5 for corn tassel branch identification; a corn tassel branch identification model based on YOLOv5-C3CA model and introduction of semi-supervised learning; corn tassel identification and branch number extraction: the YOLOv5-C3CA detection model based on semi-supervised learning is applied to identify the corn tassel; after identifying the tassel, the YOLOv5-C3CA training model based on semi-supervised learning is continuously applied to extract the number of corn tassel branches. The corn tassel identification and tassel branch identification of the application are relatively accurate, which shows that the corn tassel branch counting scheme based on semi-supervised learning is feasible, which not only ensures the detection performance of the model, but also saves the data labeling workload.
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