Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism

Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based...
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Autor*in:	Liying Cao [verfasserIn] Hongda Li [verfasserIn] Xuerui Liu [verfasserIn] Guifen Chen [verfasserIn] Helong Yu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Semantic segmentation attention mechanism generative adversarial network Segnet phenotypic characteristics

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 76310-76317
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:76310-76317

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3190347

Katalog-ID:	DOAJ030715911

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520			\|a Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based on generative adversarial network and attention mechanism to improve the accuracy of semantic segmentation of plant leaves. First, the data set is standardized and divided into a training set and test set. The generator that produces the confrontation network uses Segnet as the backbone network and adds an attention mechanism to extract the phenotypic characteristics of plants. The discriminator utilizes a dual-input fully connected layer for true and false estimate. The experimental results show that compared with the original Segnet segmentation network, the proposed strategy improves the precision of pixel recognition PA. Also, the suggested technique has a high level of robustness and feature extraction precision. In addition to providing technical assistance for future crop cultivation and breeding, monitoring crop growth, ensuring yields as well as solving the food shortage problem.
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language	English
source	In IEEE Access 10(2022), Seite 76310-76317 volume:10 year:2022 pages:76310-76317
sourceStr	In IEEE Access 10(2022), Seite 76310-76317 volume:10 year:2022 pages:76310-76317
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Semantic segmentation attention mechanism generative adversarial network Segnet phenotypic characteristics Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Liying Cao @@aut@@ Hongda Li @@aut@@ Xuerui Liu @@aut@@ Guifen Chen @@aut@@ Helong Yu @@aut@@
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callnumber-first	T - Technology
author	Liying Cao
spellingShingle	Liying Cao misc TK1-9971 misc Semantic segmentation misc attention mechanism misc generative adversarial network misc Segnet misc phenotypic characteristics misc Electrical engineering. Electronics. Nuclear engineering Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism
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topic_title	TK1-9971 Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism Semantic segmentation attention mechanism generative adversarial network Segnet phenotypic characteristics
topic	misc TK1-9971 misc Semantic segmentation misc attention mechanism misc generative adversarial network misc Segnet misc phenotypic characteristics misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Semantic segmentation misc attention mechanism misc generative adversarial network misc Segnet misc phenotypic characteristics misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Semantic segmentation misc attention mechanism misc generative adversarial network misc Segnet misc phenotypic characteristics misc Electrical engineering. Electronics. Nuclear engineering
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title	Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism
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title_full	Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism
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title_sort	semantic segmentation of plant leaves based on generative adversarial network and attention mechanism
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title_auth	Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism
abstract	Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based on generative adversarial network and attention mechanism to improve the accuracy of semantic segmentation of plant leaves. First, the data set is standardized and divided into a training set and test set. The generator that produces the confrontation network uses Segnet as the backbone network and adds an attention mechanism to extract the phenotypic characteristics of plants. The discriminator utilizes a dual-input fully connected layer for true and false estimate. The experimental results show that compared with the original Segnet segmentation network, the proposed strategy improves the precision of pixel recognition PA. Also, the suggested technique has a high level of robustness and feature extraction precision. In addition to providing technical assistance for future crop cultivation and breeding, monitoring crop growth, ensuring yields as well as solving the food shortage problem.
abstractGer	Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based on generative adversarial network and attention mechanism to improve the accuracy of semantic segmentation of plant leaves. First, the data set is standardized and divided into a training set and test set. The generator that produces the confrontation network uses Segnet as the backbone network and adds an attention mechanism to extract the phenotypic characteristics of plants. The discriminator utilizes a dual-input fully connected layer for true and false estimate. The experimental results show that compared with the original Segnet segmentation network, the proposed strategy improves the precision of pixel recognition PA. Also, the suggested technique has a high level of robustness and feature extraction precision. In addition to providing technical assistance for future crop cultivation and breeding, monitoring crop growth, ensuring yields as well as solving the food shortage problem.
abstract_unstemmed	Due to the rapid growth of the population, the pressure on food is increasing and the demand for higher crop yields is rising. It is crucial to periodically monitor plant phenotypic traits, and deep learning has a good effect on image recognition and segmentation. This paper proposes a method based on generative adversarial network and attention mechanism to improve the accuracy of semantic segmentation of plant leaves. First, the data set is standardized and divided into a training set and test set. The generator that produces the confrontation network uses Segnet as the backbone network and adds an attention mechanism to extract the phenotypic characteristics of plants. The discriminator utilizes a dual-input fully connected layer for true and false estimate. The experimental results show that compared with the original Segnet segmentation network, the proposed strategy improves the precision of pixel recognition PA. Also, the suggested technique has a high level of robustness and feature extraction precision. In addition to providing technical assistance for future crop cultivation and breeding, monitoring crop growth, ensuring yields as well as solving the food shortage problem.
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title_short	Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism
url	https://doi.org/10.1109/ACCESS.2022.3190347 https://doaj.org/article/2a42b5fda0b94aa5b2b66f51ae819c42 https://ieeexplore.ieee.org/document/9828400/ https://doaj.org/toc/2169-3536
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Semantic Segmentation of Plant Leaves Based on Generative Adversarial Network and Attention Mechanism

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