Cotton stubble detection based on wavelet decomposition and texture features

Abstract Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in orde...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yukun Yang, Jing Nie, Za Kan, Shuo Yang, Hangxing Zhao, Jingbin Li
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Machine vision Visual defect detection Stubble Wavelet decomposition Fusion feature Texture feature Plant culture SB1-1110 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/91bec50919384732a255086a4d98deca
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:91bec50919384732a255086a4d98deca
record_format	dspace
spelling	oai:doaj.org-article:91bec50919384732a255086a4d98deca2021-11-07T12:17:16ZCotton stubble detection based on wavelet decomposition and texture features10.1186/s13007-021-00809-31746-4811https://doaj.org/article/91bec50919384732a255086a4d98deca2021-11-01T00:00:00Zhttps://doi.org/10.1186/s13007-021-00809-3https://doaj.org/toc/1746-4811Abstract Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.Yukun YangJing NieZa KanShuo YangHangxing ZhaoJingbin LiBMCarticleMachine visionVisual defect detectionStubbleWavelet decompositionFusion featureTexture featurePlant cultureSB1-1110Biology (General)QH301-705.5ENPlant Methods, Vol 17, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine vision Visual defect detection Stubble Wavelet decomposition Fusion feature Texture feature Plant culture SB1-1110 Biology (General) QH301-705.5
spellingShingle	Machine vision Visual defect detection Stubble Wavelet decomposition Fusion feature Texture feature Plant culture SB1-1110 Biology (General) QH301-705.5 Yukun Yang Jing Nie Za Kan Shuo Yang Hangxing Zhao Jingbin Li Cotton stubble detection based on wavelet decomposition and texture features
description	Abstract Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.
format	article
author	Yukun Yang Jing Nie Za Kan Shuo Yang Hangxing Zhao Jingbin Li
author_facet	Yukun Yang Jing Nie Za Kan Shuo Yang Hangxing Zhao Jingbin Li
author_sort	Yukun Yang
title	Cotton stubble detection based on wavelet decomposition and texture features
title_short	Cotton stubble detection based on wavelet decomposition and texture features
title_full	Cotton stubble detection based on wavelet decomposition and texture features
title_fullStr	Cotton stubble detection based on wavelet decomposition and texture features
title_full_unstemmed	Cotton stubble detection based on wavelet decomposition and texture features
title_sort	cotton stubble detection based on wavelet decomposition and texture features
publisher	BMC
publishDate	2021
url	https://doaj.org/article/91bec50919384732a255086a4d98deca
work_keys_str_mv	AT yukunyang cottonstubbledetectionbasedonwaveletdecompositionandtexturefeatures AT jingnie cottonstubbledetectionbasedonwaveletdecompositionandtexturefeatures AT zakan cottonstubbledetectionbasedonwaveletdecompositionandtexturefeatures AT shuoyang cottonstubbledetectionbasedonwaveletdecompositionandtexturefeatures AT hangxingzhao cottonstubbledetectionbasedonwaveletdecompositionandtexturefeatures AT jingbinli cottonstubbledetectionbasedonwaveletdecompositionandtexturefeatures
_version_	1718443501956366336

Cotton stubble detection based on wavelet decomposition and texture features

Ejemplares similares