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Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks
Optimal fertilization of clover-grass fields relies on knowledge of the clover and grass fractions. This study shows how knowledge can be obtained by analyzing images collected in fields automatically. A fully convolutional neural network was trained to create a pixel-wise classification of clover,...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751073/ https://www.ncbi.nlm.nih.gov/pubmed/29258215 http://dx.doi.org/10.3390/s17122930 |
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author | Skovsen, Søren Dyrmann, Mads Mortensen, Anders Krogh Steen, Kim Arild Green, Ole Eriksen, Jørgen Gislum, René Jørgensen, Rasmus Nyholm Karstoft, Henrik |
author_facet | Skovsen, Søren Dyrmann, Mads Mortensen, Anders Krogh Steen, Kim Arild Green, Ole Eriksen, Jørgen Gislum, René Jørgensen, Rasmus Nyholm Karstoft, Henrik |
author_sort | Skovsen, Søren |
collection | PubMed |
description | Optimal fertilization of clover-grass fields relies on knowledge of the clover and grass fractions. This study shows how knowledge can be obtained by analyzing images collected in fields automatically. A fully convolutional neural network was trained to create a pixel-wise classification of clover, grass, and weeds in red, green, and blue (RGB) images of clover-grass mixtures. The estimated clover fractions of the dry matter from the images were found to be highly correlated with the real clover fractions of the dry matter, making this a cheap and non-destructive way of monitoring clover-grass fields. The network was trained solely on simulated top-down images of clover-grass fields. This enables the network to distinguish clover, grass, and weed pixels in real images. The use of simulated images for training reduces the manual labor to a few hours, as compared to more than 3000 h when all the real images are annotated for training. The network was tested on images with varied clover/grass ratios and achieved an overall pixel classification accuracy of 83.4%, while estimating the dry matter clover fraction with a standard deviation of 7.8%. |
format | Online Article Text |
id | pubmed-5751073 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-57510732018-01-10 Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks Skovsen, Søren Dyrmann, Mads Mortensen, Anders Krogh Steen, Kim Arild Green, Ole Eriksen, Jørgen Gislum, René Jørgensen, Rasmus Nyholm Karstoft, Henrik Sensors (Basel) Article Optimal fertilization of clover-grass fields relies on knowledge of the clover and grass fractions. This study shows how knowledge can be obtained by analyzing images collected in fields automatically. A fully convolutional neural network was trained to create a pixel-wise classification of clover, grass, and weeds in red, green, and blue (RGB) images of clover-grass mixtures. The estimated clover fractions of the dry matter from the images were found to be highly correlated with the real clover fractions of the dry matter, making this a cheap and non-destructive way of monitoring clover-grass fields. The network was trained solely on simulated top-down images of clover-grass fields. This enables the network to distinguish clover, grass, and weed pixels in real images. The use of simulated images for training reduces the manual labor to a few hours, as compared to more than 3000 h when all the real images are annotated for training. The network was tested on images with varied clover/grass ratios and achieved an overall pixel classification accuracy of 83.4%, while estimating the dry matter clover fraction with a standard deviation of 7.8%. MDPI 2017-12-17 /pmc/articles/PMC5751073/ /pubmed/29258215 http://dx.doi.org/10.3390/s17122930 Text en © 2017 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 Skovsen, Søren Dyrmann, Mads Mortensen, Anders Krogh Steen, Kim Arild Green, Ole Eriksen, Jørgen Gislum, René Jørgensen, Rasmus Nyholm Karstoft, Henrik Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks |
title | Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks |
title_full | Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks |
title_fullStr | Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks |
title_full_unstemmed | Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks |
title_short | Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks |
title_sort | estimation of the botanical composition of clover-grass leys from rgb images using data simulation and fully convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751073/ https://www.ncbi.nlm.nih.gov/pubmed/29258215 http://dx.doi.org/10.3390/s17122930 |
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