Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network

Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network

The viability of Zea mays seed plays a critical role in determining the yield of corn. Therefore, developing a fast and non-destructive method is essential for rapid and large-scale seed viability detection and is of great significance for agriculture, breeding, and germplasm preservation. In this s...

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Bibliographic Details
Main Authors: Yaoyao Fan, Ting An, Qingyan Wang, Guang Yang, Wenqian Huang, Zheli Wang, Chunjiang Zhao, Xi Tian
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-08-01
Series:Frontiers in Plant Science
Subjects:
viability detection
maize seeds
hyperspectral imaging
YOLOv7 model
3D convolution neural network
Online Access:https://www.frontiersin.org/articles/10.3389/fpls.2023.1248598/full

Internet

https://www.frontiersin.org/articles/10.3389/fpls.2023.1248598/full

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