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DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks

Crop yield is an essential measure for breeders, researchers, and farmers and is composed of and may be calculated by the number of ears per square meter, grains per ear, and thousand grain weight. Manual wheat ear counting, required in breeding programs to evaluate crop yield potential, is labor-in...

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Autores principales: Sadeghi-Tehran, Pouria, Virlet, Nicolas, Ampe, Eva M., Reyns, Piet, Hawkesford, Malcolm J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6775245/
https://www.ncbi.nlm.nih.gov/pubmed/31616456
http://dx.doi.org/10.3389/fpls.2019.01176
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author Sadeghi-Tehran, Pouria
Virlet, Nicolas
Ampe, Eva M.
Reyns, Piet
Hawkesford, Malcolm J.
author_facet Sadeghi-Tehran, Pouria
Virlet, Nicolas
Ampe, Eva M.
Reyns, Piet
Hawkesford, Malcolm J.
author_sort Sadeghi-Tehran, Pouria
collection PubMed
description Crop yield is an essential measure for breeders, researchers, and farmers and is composed of and may be calculated by the number of ears per square meter, grains per ear, and thousand grain weight. Manual wheat ear counting, required in breeding programs to evaluate crop yield potential, is labor-intensive and expensive; thus, the development of a real-time wheat head counting system would be a significant advancement. In this paper, we propose a computationally efficient system called DeepCount to automatically identify and count the number of wheat spikes in digital images taken under natural field conditions. The proposed method tackles wheat spike quantification by segmenting an image into superpixels using simple linear iterative clustering (SLIC), deriving canopy relevant features, and then constructing a rational feature model fed into the deep convolutional neural network (CNN) classification for semantic segmentation of wheat spikes. As the method is based on a deep learning model, it replaces hand-engineered features required for traditional machine learning methods with more efficient algorithms. The method is tested on digital images taken directly in the field at different stages of ear emergence/maturity (using visually different wheat varieties), with different canopy complexities (achieved through varying nitrogen inputs) and different heights above the canopy under varying environmental conditions. In addition, the proposed technique is compared with a wheat ear counting method based on a previously developed edge detection technique and morphological analysis. The proposed approach is validated with image-based ear counting and ground-based measurements. The results demonstrate that the DeepCount technique has a high level of robustness regardless of variables, such as growth stage and weather conditions, hence demonstrating the feasibility of the approach in real scenarios. The system is a leap toward a portable and smartphone-assisted wheat ear counting systems, results in reducing the labor involved, and is suitable for high-throughput analysis. It may also be adapted to work on Red; Green; Blue (RGB) images acquired from unmanned aerial vehicle (UAVs).
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spelling pubmed-67752452019-10-15 DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks Sadeghi-Tehran, Pouria Virlet, Nicolas Ampe, Eva M. Reyns, Piet Hawkesford, Malcolm J. Front Plant Sci Plant Science Crop yield is an essential measure for breeders, researchers, and farmers and is composed of and may be calculated by the number of ears per square meter, grains per ear, and thousand grain weight. Manual wheat ear counting, required in breeding programs to evaluate crop yield potential, is labor-intensive and expensive; thus, the development of a real-time wheat head counting system would be a significant advancement. In this paper, we propose a computationally efficient system called DeepCount to automatically identify and count the number of wheat spikes in digital images taken under natural field conditions. The proposed method tackles wheat spike quantification by segmenting an image into superpixels using simple linear iterative clustering (SLIC), deriving canopy relevant features, and then constructing a rational feature model fed into the deep convolutional neural network (CNN) classification for semantic segmentation of wheat spikes. As the method is based on a deep learning model, it replaces hand-engineered features required for traditional machine learning methods with more efficient algorithms. The method is tested on digital images taken directly in the field at different stages of ear emergence/maturity (using visually different wheat varieties), with different canopy complexities (achieved through varying nitrogen inputs) and different heights above the canopy under varying environmental conditions. In addition, the proposed technique is compared with a wheat ear counting method based on a previously developed edge detection technique and morphological analysis. The proposed approach is validated with image-based ear counting and ground-based measurements. The results demonstrate that the DeepCount technique has a high level of robustness regardless of variables, such as growth stage and weather conditions, hence demonstrating the feasibility of the approach in real scenarios. The system is a leap toward a portable and smartphone-assisted wheat ear counting systems, results in reducing the labor involved, and is suitable for high-throughput analysis. It may also be adapted to work on Red; Green; Blue (RGB) images acquired from unmanned aerial vehicle (UAVs). Frontiers Media S.A. 2019-09-26 /pmc/articles/PMC6775245/ /pubmed/31616456 http://dx.doi.org/10.3389/fpls.2019.01176 Text en Copyright © 2019 Sadeghi-Tehran, Virlet, Ampe, Reyns and Hawkesford http://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
Sadeghi-Tehran, Pouria
Virlet, Nicolas
Ampe, Eva M.
Reyns, Piet
Hawkesford, Malcolm J.
DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks
title DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks
title_full DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks
title_fullStr DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks
title_full_unstemmed DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks
title_short DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks
title_sort deepcount: in-field automatic quantification of wheat spikes using simple linear iterative clustering and deep convolutional neural networks
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6775245/
https://www.ncbi.nlm.nih.gov/pubmed/31616456
http://dx.doi.org/10.3389/fpls.2019.01176
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