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A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network
Determining the individual location of a plant, besides evaluating sowing performance, would make subsequent treatment for each plant across a field possible. In this study, a system for locating cereal plant stem emerging points (PSEPs) has been developed. In total, 5719 images were gathered from s...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982203/ https://www.ncbi.nlm.nih.gov/pubmed/29783642 http://dx.doi.org/10.3390/s18051611 |
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author | Karimi, Hadi Skovsen, Søren Dyrmann, Mads Nyholm Jørgensen, Rasmus |
author_facet | Karimi, Hadi Skovsen, Søren Dyrmann, Mads Nyholm Jørgensen, Rasmus |
author_sort | Karimi, Hadi |
collection | PubMed |
description | Determining the individual location of a plant, besides evaluating sowing performance, would make subsequent treatment for each plant across a field possible. In this study, a system for locating cereal plant stem emerging points (PSEPs) has been developed. In total, 5719 images were gathered from several cereal fields. In 212 of these images, the PSEPs of the cereal plants were marked manually and used to train a fully-convolutional neural network. In the training process, a cost function was made, which incorporates predefined penalty regions and PSEPs. The penalty regions were defined based on fault prediction of the trained model without penalty region assignment. By adding penalty regions to the training, the network’s ability to precisely locate emergence points of the cereal plants was enhanced significantly. A coefficient of determination of about 87 percent between the predicted PSEP number of each image and the manually marked one implies the ability of the system to count PSEPs. With regard to the obtained results, it was concluded that the developed model can give a reliable clue about the quality of PSEPs’ distribution and the performance of seed drills in fields. |
format | Online Article Text |
id | pubmed-5982203 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-59822032018-06-05 A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network Karimi, Hadi Skovsen, Søren Dyrmann, Mads Nyholm Jørgensen, Rasmus Sensors (Basel) Article Determining the individual location of a plant, besides evaluating sowing performance, would make subsequent treatment for each plant across a field possible. In this study, a system for locating cereal plant stem emerging points (PSEPs) has been developed. In total, 5719 images were gathered from several cereal fields. In 212 of these images, the PSEPs of the cereal plants were marked manually and used to train a fully-convolutional neural network. In the training process, a cost function was made, which incorporates predefined penalty regions and PSEPs. The penalty regions were defined based on fault prediction of the trained model without penalty region assignment. By adding penalty regions to the training, the network’s ability to precisely locate emergence points of the cereal plants was enhanced significantly. A coefficient of determination of about 87 percent between the predicted PSEP number of each image and the manually marked one implies the ability of the system to count PSEPs. With regard to the obtained results, it was concluded that the developed model can give a reliable clue about the quality of PSEPs’ distribution and the performance of seed drills in fields. MDPI 2018-05-18 /pmc/articles/PMC5982203/ /pubmed/29783642 http://dx.doi.org/10.3390/s18051611 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Karimi, Hadi Skovsen, Søren Dyrmann, Mads Nyholm Jørgensen, Rasmus A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network |
title | A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network |
title_full | A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network |
title_fullStr | A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network |
title_full_unstemmed | A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network |
title_short | A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network |
title_sort | novel locating system for cereal plant stem emerging points’ detection using a convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982203/ https://www.ncbi.nlm.nih.gov/pubmed/29783642 http://dx.doi.org/10.3390/s18051611 |
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