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SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques
The application of computer vision in agriculture has already contributed immensely to restructuring the existing field practices starting from the sowing to the harvesting. Among the different plant parts, the economic part, the yield, has the highest importance and becomes the ultimate goal for th...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386505/ https://www.ncbi.nlm.nih.gov/pubmed/35991448 http://dx.doi.org/10.3389/fpls.2022.889853 |
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author | Maji, Arpan K. Marwaha, Sudeep Kumar, Sudhir Arora, Alka Chinnusamy, Viswanathan Islam, Shahnawazul |
author_facet | Maji, Arpan K. Marwaha, Sudeep Kumar, Sudhir Arora, Alka Chinnusamy, Viswanathan Islam, Shahnawazul |
author_sort | Maji, Arpan K. |
collection | PubMed |
description | The application of computer vision in agriculture has already contributed immensely to restructuring the existing field practices starting from the sowing to the harvesting. Among the different plant parts, the economic part, the yield, has the highest importance and becomes the ultimate goal for the farming community. It depends on many genetic and environmental factors, so this curiosity about knowing the yield brought several precise pre-harvest prediction methods using different ways. Out of those techniques, non-invasive yield prediction techniques using computer vision have been proved to be the most efficient and trusted platform. This study developed a novel methodology, called SlypNet, using advanced deep learning networks, i.e., Mask R-CNN and U-Net, which can extract various plant morphological features like spike and spikelet from the visual image of the wheat plant and provide a high-throughput yield estimate with great precision. Mask R-CNN outperformed previous networks in spike detection by its precise detection performance with a mean average precision (mAP) of 97.57%, a F1 score of 0.67, and an MCC of 0.91 by overcoming several natural field constraints like overlapping and background interference, variable resolution, and high bushiness of plants. The spikelet detection module’s accuracy and consistency were tested with about 99% validation accuracy of the model and the least error, i.e., a mean square error of 1.3 from a set of typical and complex views of wheat spikes. Spikelet yield cumulatively showed the probable production capability of each plant. Our method presents an integrated deep learning platform of spikelet-based yield prediction comprising spike and spikelet detection, leading to higher precision over the existing methods. |
format | Online Article Text |
id | pubmed-9386505 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93865052022-08-19 SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques Maji, Arpan K. Marwaha, Sudeep Kumar, Sudhir Arora, Alka Chinnusamy, Viswanathan Islam, Shahnawazul Front Plant Sci Plant Science The application of computer vision in agriculture has already contributed immensely to restructuring the existing field practices starting from the sowing to the harvesting. Among the different plant parts, the economic part, the yield, has the highest importance and becomes the ultimate goal for the farming community. It depends on many genetic and environmental factors, so this curiosity about knowing the yield brought several precise pre-harvest prediction methods using different ways. Out of those techniques, non-invasive yield prediction techniques using computer vision have been proved to be the most efficient and trusted platform. This study developed a novel methodology, called SlypNet, using advanced deep learning networks, i.e., Mask R-CNN and U-Net, which can extract various plant morphological features like spike and spikelet from the visual image of the wheat plant and provide a high-throughput yield estimate with great precision. Mask R-CNN outperformed previous networks in spike detection by its precise detection performance with a mean average precision (mAP) of 97.57%, a F1 score of 0.67, and an MCC of 0.91 by overcoming several natural field constraints like overlapping and background interference, variable resolution, and high bushiness of plants. The spikelet detection module’s accuracy and consistency were tested with about 99% validation accuracy of the model and the least error, i.e., a mean square error of 1.3 from a set of typical and complex views of wheat spikes. Spikelet yield cumulatively showed the probable production capability of each plant. Our method presents an integrated deep learning platform of spikelet-based yield prediction comprising spike and spikelet detection, leading to higher precision over the existing methods. Frontiers Media S.A. 2022-08-04 /pmc/articles/PMC9386505/ /pubmed/35991448 http://dx.doi.org/10.3389/fpls.2022.889853 Text en Copyright © 2022 Maji, Marwaha, Kumar, Arora, Chinnusamy and Islam. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Maji, Arpan K. Marwaha, Sudeep Kumar, Sudhir Arora, Alka Chinnusamy, Viswanathan Islam, Shahnawazul SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques |
title | SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques |
title_full | SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques |
title_fullStr | SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques |
title_full_unstemmed | SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques |
title_short | SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques |
title_sort | slypnet: spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386505/ https://www.ncbi.nlm.nih.gov/pubmed/35991448 http://dx.doi.org/10.3389/fpls.2022.889853 |
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