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http://krishi.icar.gov.in/jspui/handle/123456789/47163| Title: | SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging |
| Other Titles: | Not Available |
| Authors: | Tanuj Misra Alka Arora Sudeep Marwaha Viswanathan Chinnusamy2 A.R. Rao Rajni Jain Rabi Narayan Sahoo Mrinmoy Ray Sudhir Kumar Dhandapani Raju Ranjeet Ranjan Jha Aditya Nigam Swati Goel |
| ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
| Author's Affiliated institute: | ICAR::Indian Agricultural Statistics Research Institute ICAR::National Institute of Agricultural Economics and Policy Research ICAR::Indian Agricultural Research Institute Indian Institute of Technology, Mandi, Himachal Pradesh, India. |
| Published/ Complete Date: | 2020-03-18 |
| Project Code: | Not Available |
| Keywords: | Deep learning Encoder-decoder deep network Image analysis Non-destructive plant phenotyping Wheat spikes identification and count |
| Publisher: | BMC |
| Citation: | Misra, T., Arora, A., Marwaha, S., Chinnusamy, V., Rao, A. R., Jain, R., ... & Goel, S. (2020). SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging. Plant methods, 16(1), 1-20. |
| Series/Report no.: | Not Available; |
| Abstract/Description: | 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 for phenomics of a large set of germplasm and breeding lines in controlled and field conditions. It is also required for precision agriculture where the application of nitrogen, water, and other inputs at this critical stage is necessary. Further, counting of spikes is an important measure to determine yield. Digital image analysis and machine learning techniques play an essential role in non-destructive plant phenotyping analysis. |
| Description: | Not Available |
| ISSN: | Not Available |
| Type(s) of content: | Research Paper |
| Sponsors: | Not Available |
| Language: | English |
| Name of Journal: | Plant Methods |
| NAAS Rating: | Not Available |
| Volume No.: | 16(1) |
| Page Number: | 1-20 |
| Name of the Division/Regional Station: | Not Available |
| Source, DOI or any other URL: | https://doi.org/10.1186/s13007-020-00582-9 |
| URI: | http://krishi.icar.gov.in/jspui/handle/123456789/47163 |
| Appears in Collections: | AEdu-IASRI-Publication |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2. Misra2020_Article_SpikeSegNet-aDeepLearningAppro.pdf | 2 MB | Adobe PDF | View/Open |
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