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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-03-01
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| Series: | Plant Methods |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13007-025-01363-y |
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