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Automated Counting Grains on the Rice Panicle Based on Deep Learning Method

Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was propo...

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Autores principales: Deng, Ruoling, Tao, Ming, Huang, Xunan, Bangura, Kemoh, Jiang, Qian, Jiang, Yu, Qi, Long
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795532/
https://www.ncbi.nlm.nih.gov/pubmed/33406615
http://dx.doi.org/10.3390/s21010281
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author Deng, Ruoling
Tao, Ming
Huang, Xunan
Bangura, Kemoh
Jiang, Qian
Jiang, Yu
Qi, Long
author_facet Deng, Ruoling
Tao, Ming
Huang, Xunan
Bangura, Kemoh
Jiang, Qian
Jiang, Yu
Qi, Long
author_sort Deng, Ruoling
collection PubMed
description Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions.
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spelling pubmed-77955322021-01-10 Automated Counting Grains on the Rice Panicle Based on Deep Learning Method Deng, Ruoling Tao, Ming Huang, Xunan Bangura, Kemoh Jiang, Qian Jiang, Yu Qi, Long Sensors (Basel) Article Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions. MDPI 2021-01-04 /pmc/articles/PMC7795532/ /pubmed/33406615 http://dx.doi.org/10.3390/s21010281 Text en © 2021 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
Deng, Ruoling
Tao, Ming
Huang, Xunan
Bangura, Kemoh
Jiang, Qian
Jiang, Yu
Qi, Long
Automated Counting Grains on the Rice Panicle Based on Deep Learning Method
title Automated Counting Grains on the Rice Panicle Based on Deep Learning Method
title_full Automated Counting Grains on the Rice Panicle Based on Deep Learning Method
title_fullStr Automated Counting Grains on the Rice Panicle Based on Deep Learning Method
title_full_unstemmed Automated Counting Grains on the Rice Panicle Based on Deep Learning Method
title_short Automated Counting Grains on the Rice Panicle Based on Deep Learning Method
title_sort automated counting grains on the rice panicle based on deep learning method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795532/
https://www.ncbi.nlm.nih.gov/pubmed/33406615
http://dx.doi.org/10.3390/s21010281
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