SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging

SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging

Abstract Background High throughput non-destructive phenotyping is emerging as a significant approach for phenotyping germplasm and breeding populations for the identification of superior donors, elite lines, and QTLs. Detection and counting of spikes, the grain bearing organs of wheat, is critical...

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Bibliographic Details
Main Authors: Tanuj Misra, Alka Arora, Sudeep Marwaha, Viswanathan Chinnusamy, Atmakuri Ramakrishna Rao, Rajni Jain, Rabi Narayan Sahoo, Mrinmoy Ray, Sudhir Kumar, Dhandapani Raju, Ranjeet Ranjan Jha, Aditya Nigam, Swati Goel
Format: Article
Language:English
Published: BMC 2020-03-01
Series:Plant Methods
Subjects:
Deep learning
Encoder-decoder deep network
Image analysis
Non-destructive plant phenotyping
Wheat spikes identification and count
Online Access:http://link.springer.com/article/10.1186/s13007-020-00582-9

Internet

http://link.springer.com/article/10.1186/s13007-020-00582-9

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