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Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model

In recent years, many image-based approaches have been proposed to classify plant species. Most methods utilized red green blue (RGB) imaging materials and designed custom features to classify the plant images using machine learning algorithms. Those works primarily focused on analyzing single-leaf...

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Autores principales: Liu, Keng-Hao, Yang, Meng-Hsien, Huang, Sheng-Ting, Lin, Chinsu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044035/
https://www.ncbi.nlm.nih.gov/pubmed/35498669
http://dx.doi.org/10.3389/fpls.2022.855660
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author Liu, Keng-Hao
Yang, Meng-Hsien
Huang, Sheng-Ting
Lin, Chinsu
author_facet Liu, Keng-Hao
Yang, Meng-Hsien
Huang, Sheng-Ting
Lin, Chinsu
author_sort Liu, Keng-Hao
collection PubMed
description In recent years, many image-based approaches have been proposed to classify plant species. Most methods utilized red green blue (RGB) imaging materials and designed custom features to classify the plant images using machine learning algorithms. Those works primarily focused on analyzing single-leaf images instead of live-crown images. Without considering the additional features of the leaves’ color and spatial pattern, they failed to handle cases that contained leaves similar in appearance due to the limited spectral information of RGB imaging. To tackle this dilemma, this study proposes a novel framework that combines hyperspectral imaging (HSI) and deep learning techniques for plant image classification. We built a plant image dataset containing 1,500 images of 30 different plant species taken by a 470–900 nm hyperspectral camera and designed a lightweight conventional neural network (CNN) model (LtCNN) to perform image classification. Several state-of-art CNN classifiers are chosen for comparison. The impact of using different band combinations as the network input is also investigated. Results show that using simulated RGB images achieves a kappa coefficient of nearly 0.90 while using the combination of 3-band RGB and 3-band near-infrared images can improve to 0.95. It is also found that the proposed LtCNN can obtain a satisfactory performance of plant classification (kappa = 0.95) using critical spectral features of the green edge (591 nm), red-edge (682 nm), and near-infrared (762 nm) bands. This study also demonstrates the excellent adaptability of the LtCNN model in recognizing leaf features of plant live-crown images while using a relatively smaller number of training samples than complex CNN models such as AlexNet, GoogLeNet, and VGGNet.
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spelling pubmed-90440352022-04-28 Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model Liu, Keng-Hao Yang, Meng-Hsien Huang, Sheng-Ting Lin, Chinsu Front Plant Sci Plant Science In recent years, many image-based approaches have been proposed to classify plant species. Most methods utilized red green blue (RGB) imaging materials and designed custom features to classify the plant images using machine learning algorithms. Those works primarily focused on analyzing single-leaf images instead of live-crown images. Without considering the additional features of the leaves’ color and spatial pattern, they failed to handle cases that contained leaves similar in appearance due to the limited spectral information of RGB imaging. To tackle this dilemma, this study proposes a novel framework that combines hyperspectral imaging (HSI) and deep learning techniques for plant image classification. We built a plant image dataset containing 1,500 images of 30 different plant species taken by a 470–900 nm hyperspectral camera and designed a lightweight conventional neural network (CNN) model (LtCNN) to perform image classification. Several state-of-art CNN classifiers are chosen for comparison. The impact of using different band combinations as the network input is also investigated. Results show that using simulated RGB images achieves a kappa coefficient of nearly 0.90 while using the combination of 3-band RGB and 3-band near-infrared images can improve to 0.95. It is also found that the proposed LtCNN can obtain a satisfactory performance of plant classification (kappa = 0.95) using critical spectral features of the green edge (591 nm), red-edge (682 nm), and near-infrared (762 nm) bands. This study also demonstrates the excellent adaptability of the LtCNN model in recognizing leaf features of plant live-crown images while using a relatively smaller number of training samples than complex CNN models such as AlexNet, GoogLeNet, and VGGNet. Frontiers Media S.A. 2022-04-13 /pmc/articles/PMC9044035/ /pubmed/35498669 http://dx.doi.org/10.3389/fpls.2022.855660 Text en Copyright © 2022 Liu, Yang, Huang and Lin. https://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
Liu, Keng-Hao
Yang, Meng-Hsien
Huang, Sheng-Ting
Lin, Chinsu
Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model
title Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model
title_full Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model
title_fullStr Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model
title_full_unstemmed Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model
title_short Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model
title_sort plant species classification based on hyperspectral imaging via a lightweight convolutional neural network model
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044035/
https://www.ncbi.nlm.nih.gov/pubmed/35498669
http://dx.doi.org/10.3389/fpls.2022.855660
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