Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Maji, Arpan K., Marwaha, Sudeep, Kumar, Sudhir, Arora, Alka, Chinnusamy, Viswanathan, Islam, Shahnawazul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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
_version_ 1784769827004481536
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
work_keys_str_mv AT majiarpank slypnetspikeletbasedyieldpredictionofwheatusingadvancedplantphenotypingandcomputervisiontechniques
AT marwahasudeep slypnetspikeletbasedyieldpredictionofwheatusingadvancedplantphenotypingandcomputervisiontechniques
AT kumarsudhir slypnetspikeletbasedyieldpredictionofwheatusingadvancedplantphenotypingandcomputervisiontechniques
AT aroraalka slypnetspikeletbasedyieldpredictionofwheatusingadvancedplantphenotypingandcomputervisiontechniques
AT chinnusamyviswanathan slypnetspikeletbasedyieldpredictionofwheatusingadvancedplantphenotypingandcomputervisiontechniques
AT islamshahnawazul slypnetspikeletbasedyieldpredictionofwheatusingadvancedplantphenotypingandcomputervisiontechniques