Cargando…

Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine

Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining mu...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Yajun, Wu, Zhangnan, Zhao, Bo, Fan, Caixia, Shi, Shuwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796182/
https://www.ncbi.nlm.nih.gov/pubmed/33396255
http://dx.doi.org/10.3390/s21010212
_version_ 1783634621759488000
author Chen, Yajun
Wu, Zhangnan
Zhao, Bo
Fan, Caixia
Shi, Shuwei
author_facet Chen, Yajun
Wu, Zhangnan
Zhao, Bo
Fan, Caixia
Shi, Shuwei
author_sort Chen, Yajun
collection PubMed
description Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify and detect the position of corn seedlings and weeds, to reduce the harm of weeds on corn growth, and to achieve accurate fertilization, thereby realizing precise weeding or fertilizing. First, the image dataset for weed and corn seedling classification in the corn seedling stage was established. Second, many different features of corn seedlings and weeds were extracted, and dimensionality was reduced by principal component analysis, including the histogram of oriented gradient feature, rotation invariant local binary pattern (LBP) feature, Hu invariant moment feature, Gabor feature, gray level co-occurrence matrix, and gray level-gradient co-occurrence matrix. Then, the classifier training based on SVM was conducted to obtain the recognition model for corn seedlings and weeds. The comprehensive recognition performance of single feature or different fusion strategies for six features is compared and analyzed, and the optimal feature fusion strategy is obtained. Finally, by utilizing the actual corn seedling field images, the proposed weed and corn seedling detection method effect was tested. LAB color space and K-means clustering were used to achieve image segmentation. Connected component analysis was adopted to remove small objects. The previously trained recognition model was utilized to identify and label each connected region to identify and detect weeds and corn seedlings. The experimental results showed that the fusion feature combination of rotation invariant LBP feature and gray level-gradient co-occurrence matrix based on SVM classifier obtained the highest classification accuracy and accurately detected all kinds of weeds and corn seedlings. It provided information on weed and crop positions to the spraying herbicide robot for accurate spraying or to the precise fertilization machine for accurate fertilizing.
format Online
Article
Text
id pubmed-7796182
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-77961822021-01-10 Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine Chen, Yajun Wu, Zhangnan Zhao, Bo Fan, Caixia Shi, Shuwei Sensors (Basel) Article Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify and detect the position of corn seedlings and weeds, to reduce the harm of weeds on corn growth, and to achieve accurate fertilization, thereby realizing precise weeding or fertilizing. First, the image dataset for weed and corn seedling classification in the corn seedling stage was established. Second, many different features of corn seedlings and weeds were extracted, and dimensionality was reduced by principal component analysis, including the histogram of oriented gradient feature, rotation invariant local binary pattern (LBP) feature, Hu invariant moment feature, Gabor feature, gray level co-occurrence matrix, and gray level-gradient co-occurrence matrix. Then, the classifier training based on SVM was conducted to obtain the recognition model for corn seedlings and weeds. The comprehensive recognition performance of single feature or different fusion strategies for six features is compared and analyzed, and the optimal feature fusion strategy is obtained. Finally, by utilizing the actual corn seedling field images, the proposed weed and corn seedling detection method effect was tested. LAB color space and K-means clustering were used to achieve image segmentation. Connected component analysis was adopted to remove small objects. The previously trained recognition model was utilized to identify and label each connected region to identify and detect weeds and corn seedlings. The experimental results showed that the fusion feature combination of rotation invariant LBP feature and gray level-gradient co-occurrence matrix based on SVM classifier obtained the highest classification accuracy and accurately detected all kinds of weeds and corn seedlings. It provided information on weed and crop positions to the spraying herbicide robot for accurate spraying or to the precise fertilization machine for accurate fertilizing. MDPI 2020-12-31 /pmc/articles/PMC7796182/ /pubmed/33396255 http://dx.doi.org/10.3390/s21010212 Text en © 2020 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
Chen, Yajun
Wu, Zhangnan
Zhao, Bo
Fan, Caixia
Shi, Shuwei
Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine
title Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine
title_full Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine
title_fullStr Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine
title_full_unstemmed Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine
title_short Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine
title_sort weed and corn seedling detection in field based on multi feature fusion and support vector machine
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796182/
https://www.ncbi.nlm.nih.gov/pubmed/33396255
http://dx.doi.org/10.3390/s21010212
work_keys_str_mv AT chenyajun weedandcornseedlingdetectioninfieldbasedonmultifeaturefusionandsupportvectormachine
AT wuzhangnan weedandcornseedlingdetectioninfieldbasedonmultifeaturefusionandsupportvectormachine
AT zhaobo weedandcornseedlingdetectioninfieldbasedonmultifeaturefusionandsupportvectormachine
AT fancaixia weedandcornseedlingdetectioninfieldbasedonmultifeaturefusionandsupportvectormachine
AT shishuwei weedandcornseedlingdetectioninfieldbasedonmultifeaturefusionandsupportvectormachine