Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review
Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resul...
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Series: | Plant Phenomics |
Online Access: | http://dx.doi.org/10.34133/2020/4152816 |
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doaj-7da977c8e2df44729ad613b3901b32962020-11-25T02:21:36ZengAmerican Association for the Advancement of SciencePlant Phenomics2643-65152020-01-01202010.34133/2020/4152816Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A ReviewYu Jiang0Yu Jiang1Yu Jiang2Changying Li3Changying Li4Horticulture Section,School of Integrative Plant Science,Cornell AgriTech,Cornell University,USASchool of Electrical and Computer Engineering,College of Engineering,The University of Georgia,USAPhenomics and Plant Robotics Center,The University of Georgia,USASchool of Electrical and Computer Engineering,College of Engineering,The University of Georgia,USAPhenomics and Plant Robotics Center,The University of Georgia,USAPlant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.http://dx.doi.org/10.34133/2020/4152816 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yu Jiang Yu Jiang Yu Jiang Changying Li Changying Li |
spellingShingle |
Yu Jiang Yu Jiang Yu Jiang Changying Li Changying Li Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review Plant Phenomics |
author_facet |
Yu Jiang Yu Jiang Yu Jiang Changying Li Changying Li |
author_sort |
Yu Jiang |
title |
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review |
title_short |
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review |
title_full |
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review |
title_fullStr |
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review |
title_full_unstemmed |
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review |
title_sort |
convolutional neural networks for image-based high-throughput plant phenotyping: a review |
publisher |
American Association for the Advancement of Science |
series |
Plant Phenomics |
issn |
2643-6515 |
publishDate |
2020-01-01 |
description |
Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes. |
url |
http://dx.doi.org/10.34133/2020/4152816 |
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