GrainNet: efficient detection and counting of wheat grains based on an improved YOLOv7 modeling

GrainNet: efficient detection and counting of wheat grains based on an improved YOLOv7 modeling

Abstract Background Seed testing plays a crucial role in improving crop yields.In actual seed testing processes, factors such as grain sticking and complex imaging environments can significantly affect the accuracy of wheat grain counting, directly impacting the effectiveness of seed testing. Howeve...

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Bibliographic Details
Main Authors: Xin Wang, Changchun Li, Chenyi Zhao, Yinghua Jiao, Hengmao Xiang, Xifang Wu, Huabin Chai
Format: Article
Language:English
Published: BMC 2025-03-01
Series:Plant Methods
Subjects:
Wheat seed
Deep learning
Count
YOLOv7
Target recognition
Online Access:https://doi.org/10.1186/s13007-025-01363-y
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https://doi.org/10.1186/s13007-025-01363-y

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