Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants
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 lar...
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2021-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/9432817/ |
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| author | Tanuj Misra Alka Arora Sudeep Marwaha Ranjeet Ranjan Jha Mrinmoy Ray Rajni Jain A. R. Rao Eldho Varghese Shailendra Kumar Sudhir Kumar Aditya Nigam Rabi Narayan Sahoo Viswanathan Chinnusamy |
| author_facet | Tanuj Misra Alka Arora Sudeep Marwaha Ranjeet Ranjan Jha Mrinmoy Ray Rajni Jain A. R. Rao Eldho Varghese Shailendra Kumar Sudhir Kumar Aditya Nigam Rabi Narayan Sahoo Viswanathan Chinnusamy |
| author_sort | Tanuj Misra |
| collection | DOAJ |
| 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 imageJ 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 F<sub>1</sub> 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. |
| format | Article |
| id | doaj-art-d1936b60753a4ef59eab2ac0f25c6365 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d1936b60753a4ef59eab2ac0f25c63652025-08-25T23:00:50ZengIEEEIEEE Access2169-35362021-01-019762357624710.1109/ACCESS.2021.30808369432817Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat PlantsTanuj Misra0Alka Arora1https://orcid.org/0000-0003-0999-1077Sudeep Marwaha2Ranjeet Ranjan Jha3Mrinmoy Ray4Rajni Jain5https://orcid.org/0000-0002-8493-7858A. R. Rao6Eldho Varghese7Shailendra Kumar8Sudhir Kumar9https://orcid.org/0000-0002-1089-7435Aditya Nigam10https://orcid.org/0000-0003-4755-0619Rabi Narayan Sahoo11Viswanathan Chinnusamy12ICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaSchool of Computing and Electrical Engineering (SCEE), IIT Mandi, Mandi, IndiaICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaICAR—National Institute of Agricultural Economics and Policy Research (NIAP), New Delhi, IndiaICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaICAR-Central Marine Fisheries Research Institute, Kochi, IndiaDepartment of Computer Science, Rani Lakshmi Bai Central Agricultural University, Jhansi, IndiaICAR-Indian Agricultural Research Institute, Library Avenue, New Delhi, IndiaSchool of Computing and Electrical Engineering (SCEE), IIT Mandi, Mandi, IndiaICAR-Indian Agricultural Research Institute, Library Avenue, New Delhi, IndiaICAR-Indian Agricultural Research Institute, Library Avenue, New Delhi, IndiaComputer 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 imageJ 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 F<sub>1</sub> 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.https://ieeexplore.ieee.org/document/9432817/Computer visiondeep learningdeep encoder-decoderhourglassimage analysisspike detection and counting |
| spellingShingle | Tanuj Misra Alka Arora Sudeep Marwaha Ranjeet Ranjan Jha Mrinmoy Ray Rajni Jain A. R. Rao Eldho Varghese Shailendra Kumar Sudhir Kumar Aditya Nigam Rabi Narayan Sahoo Viswanathan Chinnusamy Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants IEEE Access Computer vision deep learning deep encoder-decoder hourglass image analysis spike detection and counting |
| title | Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants |
| title_full | Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants |
| title_fullStr | Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants |
| title_full_unstemmed | Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants |
| title_short | Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants |
| title_sort | web spikesegnet deep learning framework for recognition and counting of spikes from visual images of wheat plants |
| topic | Computer vision deep learning deep encoder-decoder hourglass image analysis spike detection and counting |
| url | https://ieeexplore.ieee.org/document/9432817/ |
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