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Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants

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Relation http://eprints.cmfri.org.in/15134/
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9432817
10.1109/ACCESS.2021.3080836
 
Title Web-SpikeSegNet: Deep Learning Framework for
Recognition and Counting of Spikes From
Visual Images of Wheat Plants
 
Creator Misra, Tanuj
Arora, Alka
Marwaha, Sudeep
Jha, Ranjeet Ranjan
Ray, Mrinmoy
Jain, Rajni
Rao, A R
Varghese, Eldho
Kumar, Shailendra
Kumar, Sudhir
Nigam, Aditya
Sahoo, R N
Viswanathan, Chinnusamy
 
Description Computer vision with deep learning is emerging as a significant approach for non-invasive
and non-destructive plant phenotyping. Spikes are the reproductive organs of wheat plants. Detection and
counting of spikes considered the grain-bearing organ have great importance in the phenomics study of
large sets of germplasms. In the present study, we developed an online platform, ``Web-SpikeSegNet,'' based
on a deep-learning framework for spike detection and counting from the wheat plant's visual images. The
architecture of the Web-SpikeSegNet consists of 2 layers. First Layer, Client-Side Interface Layer, deals
with end user's requests and corresponding responses management. In contrast, the second layer, Server Side
Application Layer, consists of a spike detection and counting module. The backbone of the spike detection
module comprises of deep encoder-decoder network with hourglass network for spike segmentation. The
Spike counting module implements the ``Analyze Particle'' function of images to count the number of spikes.
For evaluating the performance of Web-SpikeSegNet, we acquired the wheat plant's visual images, and
the satisfactory segmentation performances were obtained as Type I error 0.00159, Type II error 0.0586,
Accuracy 99.65%, Precision 99.59% and F1 score 99.65%. As spike detection and counting in wheat
phenotyping are closely related to the yield, Web-SpikeSegNet is a significant step forward in the field
of wheat phenotyping and will be very useful to the researchers and students working in the domain.
 
Date 2021
 
Type Article
PeerReviewed
 
Format text
 
Language en
 
Identifier http://eprints.cmfri.org.in/15134/1/IEEE_2021_Eldho%20Varghese.pdf
Misra, Tanuj and Arora, Alka and Marwaha, Sudeep and Jha, Ranjeet Ranjan and Ray, Mrinmoy and Jain, Rajni and Rao, A R and Varghese, Eldho and Kumar, Shailendra and Kumar, Sudhir and Nigam, Aditya and Sahoo, R N and Viswanathan, Chinnusamy (2021) Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants. IEEE Access. pp. 1-13.