Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology

An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.094</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula< in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.113</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula<. The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<140</mn< <mtext< </mtext< <mo<×</mo< <mtext< </mtext< <mn<260</mn< </mrow< </semantics< </math< </inline-formula< pixels could achieve high-precision vision navigation while the course deviation angle was not more than <inline-formula< <math display="inline"< <semantics< <mrow< <mn<7.5</mn< <mo<°</mo< </mrow< </semantics< </math< </inline-formula<. The maximum tractor speed of the optimal template and global template were <inline-formula< <math display="inline"< <semantics< <mrow< <mn<51.41</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula< and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<27.47</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula<, respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wei Lu [verfasserIn] Mengjie Zeng [verfasserIn] Ling Wang [verfasserIn] Hui Luo [verfasserIn] Subrata Mukherjee [verfasserIn] Xuhui Huang [verfasserIn] Yiming Deng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	intelligent tractor vision navigation improved anti-noise morphology boundary line guided filtering

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 19(2019), 18, p 3918
Übergeordnetes Werk:	volume:19 ; year:2019 ; number:18, p 3918

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s19183918

Katalog-ID:	DOAJ029715342

Internformat


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245	1	0	\|a Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology
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520			\|a An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.094</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula< in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.113</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula<. The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<140</mn< <mtext< </mtext< <mo<×</mo< <mtext< </mtext< <mn<260</mn< </mrow< </semantics< </math< </inline-formula< pixels could achieve high-precision vision navigation while the course deviation angle was not more than <inline-formula< <math display="inline"< <semantics< <mrow< <mn<7.5</mn< <mo<°</mo< </mrow< </semantics< </math< </inline-formula<. The maximum tractor speed of the optimal template and global template were <inline-formula< <math display="inline"< <semantics< <mrow< <mn<51.41</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula< and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<27.47</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula<, respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.
650		4	\|a intelligent tractor
650		4	\|a vision navigation
650		4	\|a improved anti-noise morphology
650		4	\|a boundary line
650		4	\|a guided filtering
653		0	\|a Chemical technology
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700	0		\|a Ling Wang \|e verfasserin \|4 aut
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700	0		\|a Subrata Mukherjee \|e verfasserin \|4 aut
700	0		\|a Xuhui Huang \|e verfasserin \|4 aut
700	0		\|a Yiming Deng \|e verfasserin \|4 aut
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952			\|d 19 \|j 2019 \|e 18, p 3918

Indexfelder

author_variant	w l wl m z mz l w lw h l hl s m sm x h xh y d yd
matchkey_str	article:14248220:2019----::aiainloihbsdnhbudrlnotlaeolobndihudditrn
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allfields	10.3390/s19183918 doi (DE-627)DOAJ029715342 (DE-599)DOAJd5c09ad3121a4f88bd377ba061d1b9c4 DE-627 ger DE-627 rakwb eng TP1-1185 Wei Lu verfasserin aut Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.094</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula< in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.113</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula<. The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<140</mn< <mtext< </mtext< <mo<×</mo< <mtext< </mtext< <mn<260</mn< </mrow< </semantics< </math< </inline-formula< pixels could achieve high-precision vision navigation while the course deviation angle was not more than <inline-formula< <math display="inline"< <semantics< <mrow< <mn<7.5</mn< <mo<°</mo< </mrow< </semantics< </math< </inline-formula<. The maximum tractor speed of the optimal template and global template were <inline-formula< <math display="inline"< <semantics< <mrow< <mn<51.41</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula< and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<27.47</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula<, respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect. intelligent tractor vision navigation improved anti-noise morphology boundary line guided filtering Chemical technology Mengjie Zeng verfasserin aut Ling Wang verfasserin aut Hui Luo verfasserin aut Subrata Mukherjee verfasserin aut Xuhui Huang verfasserin aut Yiming Deng verfasserin aut In Sensors MDPI AG, 2003 19(2019), 18, p 3918 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:18, p 3918 https://doi.org/10.3390/s19183918 kostenfrei https://doaj.org/article/d5c09ad3121a4f88bd377ba061d1b9c4 kostenfrei https://www.mdpi.com/1424-8220/19/18/3918 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 18, p 3918
spelling	10.3390/s19183918 doi (DE-627)DOAJ029715342 (DE-599)DOAJd5c09ad3121a4f88bd377ba061d1b9c4 DE-627 ger DE-627 rakwb eng TP1-1185 Wei Lu verfasserin aut Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.094</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula< in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.113</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula<. The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<140</mn< <mtext< </mtext< <mo<×</mo< <mtext< </mtext< <mn<260</mn< </mrow< </semantics< </math< </inline-formula< pixels could achieve high-precision vision navigation while the course deviation angle was not more than <inline-formula< <math display="inline"< <semantics< <mrow< <mn<7.5</mn< <mo<°</mo< </mrow< </semantics< </math< </inline-formula<. The maximum tractor speed of the optimal template and global template were <inline-formula< <math display="inline"< <semantics< <mrow< <mn<51.41</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula< and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<27.47</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula<, respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect. intelligent tractor vision navigation improved anti-noise morphology boundary line guided filtering Chemical technology Mengjie Zeng verfasserin aut Ling Wang verfasserin aut Hui Luo verfasserin aut Subrata Mukherjee verfasserin aut Xuhui Huang verfasserin aut Yiming Deng verfasserin aut In Sensors MDPI AG, 2003 19(2019), 18, p 3918 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:18, p 3918 https://doi.org/10.3390/s19183918 kostenfrei https://doaj.org/article/d5c09ad3121a4f88bd377ba061d1b9c4 kostenfrei https://www.mdpi.com/1424-8220/19/18/3918 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 18, p 3918
allfields_unstemmed	10.3390/s19183918 doi (DE-627)DOAJ029715342 (DE-599)DOAJd5c09ad3121a4f88bd377ba061d1b9c4 DE-627 ger DE-627 rakwb eng TP1-1185 Wei Lu verfasserin aut Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.094</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula< in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.113</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula<. The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<140</mn< <mtext< </mtext< <mo<×</mo< <mtext< </mtext< <mn<260</mn< </mrow< </semantics< </math< </inline-formula< pixels could achieve high-precision vision navigation while the course deviation angle was not more than <inline-formula< <math display="inline"< <semantics< <mrow< <mn<7.5</mn< <mo<°</mo< </mrow< </semantics< </math< </inline-formula<. The maximum tractor speed of the optimal template and global template were <inline-formula< <math display="inline"< <semantics< <mrow< <mn<51.41</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula< and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<27.47</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula<, respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect. intelligent tractor vision navigation improved anti-noise morphology boundary line guided filtering Chemical technology Mengjie Zeng verfasserin aut Ling Wang verfasserin aut Hui Luo verfasserin aut Subrata Mukherjee verfasserin aut Xuhui Huang verfasserin aut Yiming Deng verfasserin aut In Sensors MDPI AG, 2003 19(2019), 18, p 3918 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:18, p 3918 https://doi.org/10.3390/s19183918 kostenfrei https://doaj.org/article/d5c09ad3121a4f88bd377ba061d1b9c4 kostenfrei https://www.mdpi.com/1424-8220/19/18/3918 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 18, p 3918
allfieldsGer	10.3390/s19183918 doi (DE-627)DOAJ029715342 (DE-599)DOAJd5c09ad3121a4f88bd377ba061d1b9c4 DE-627 ger DE-627 rakwb eng TP1-1185 Wei Lu verfasserin aut Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.094</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula< in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.113</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula<. The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<140</mn< <mtext< </mtext< <mo<×</mo< <mtext< </mtext< <mn<260</mn< </mrow< </semantics< </math< </inline-formula< pixels could achieve high-precision vision navigation while the course deviation angle was not more than <inline-formula< <math display="inline"< <semantics< <mrow< <mn<7.5</mn< <mo<°</mo< </mrow< </semantics< </math< </inline-formula<. The maximum tractor speed of the optimal template and global template were <inline-formula< <math display="inline"< <semantics< <mrow< <mn<51.41</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula< and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<27.47</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula<, respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect. intelligent tractor vision navigation improved anti-noise morphology boundary line guided filtering Chemical technology Mengjie Zeng verfasserin aut Ling Wang verfasserin aut Hui Luo verfasserin aut Subrata Mukherjee verfasserin aut Xuhui Huang verfasserin aut Yiming Deng verfasserin aut In Sensors MDPI AG, 2003 19(2019), 18, p 3918 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:18, p 3918 https://doi.org/10.3390/s19183918 kostenfrei https://doaj.org/article/d5c09ad3121a4f88bd377ba061d1b9c4 kostenfrei https://www.mdpi.com/1424-8220/19/18/3918 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 18, p 3918
allfieldsSound	10.3390/s19183918 doi (DE-627)DOAJ029715342 (DE-599)DOAJd5c09ad3121a4f88bd377ba061d1b9c4 DE-627 ger DE-627 rakwb eng TP1-1185 Wei Lu verfasserin aut Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.094</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula< in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.113</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula<. The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<140</mn< <mtext< </mtext< <mo<×</mo< <mtext< </mtext< <mn<260</mn< </mrow< </semantics< </math< </inline-formula< pixels could achieve high-precision vision navigation while the course deviation angle was not more than <inline-formula< <math display="inline"< <semantics< <mrow< <mn<7.5</mn< <mo<°</mo< </mrow< </semantics< </math< </inline-formula<. The maximum tractor speed of the optimal template and global template were <inline-formula< <math display="inline"< <semantics< <mrow< <mn<51.41</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula< and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<27.47</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula<, respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect. intelligent tractor vision navigation improved anti-noise morphology boundary line guided filtering Chemical technology Mengjie Zeng verfasserin aut Ling Wang verfasserin aut Hui Luo verfasserin aut Subrata Mukherjee verfasserin aut Xuhui Huang verfasserin aut Yiming Deng verfasserin aut In Sensors MDPI AG, 2003 19(2019), 18, p 3918 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:18, p 3918 https://doi.org/10.3390/s19183918 kostenfrei https://doaj.org/article/d5c09ad3121a4f88bd377ba061d1b9c4 kostenfrei https://www.mdpi.com/1424-8220/19/18/3918 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 18, p 3918
language	English
source	In Sensors 19(2019), 18, p 3918 volume:19 year:2019 number:18, p 3918
sourceStr	In Sensors 19(2019), 18, p 3918 volume:19 year:2019 number:18, p 3918
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	intelligent tractor vision navigation improved anti-noise morphology boundary line guided filtering Chemical technology
isfreeaccess_bool	true
container_title	Sensors
authorswithroles_txt_mv	Wei Lu @@aut@@ Mengjie Zeng @@aut@@ Ling Wang @@aut@@ Hui Luo @@aut@@ Subrata Mukherjee @@aut@@ Xuhui Huang @@aut@@ Yiming Deng @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	331640910
id	DOAJ029715342
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ029715342</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230307140447.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/s19183918</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ029715342</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJd5c09ad3121a4f88bd377ba061d1b9c4</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP1-1185</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Wei Lu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.094</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula< in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.113</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula<. The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<140</mn< <mtext< </mtext< <mo<×</mo< <mtext< </mtext< <mn<260</mn< </mrow< </semantics< </math< </inline-formula< pixels could achieve high-precision vision navigation while the course deviation angle was not more than <inline-formula< <math display="inline"< <semantics< <mrow< <mn<7.5</mn< <mo<°</mo< </mrow< </semantics< </math< </inline-formula<. The maximum tractor speed of the optimal template and global template were <inline-formula< <math display="inline"< <semantics< <mrow< <mn<51.41</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula< and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<27.47</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula<, respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. 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callnumber-first	T - Technology
author	Wei Lu
spellingShingle	Wei Lu misc TP1-1185 misc intelligent tractor misc vision navigation misc improved anti-noise morphology misc boundary line misc guided filtering misc Chemical technology Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology
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topic_title	TP1-1185 Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology intelligent tractor vision navigation improved anti-noise morphology boundary line guided filtering
topic	misc TP1-1185 misc intelligent tractor misc vision navigation misc improved anti-noise morphology misc boundary line misc guided filtering misc Chemical technology
topic_unstemmed	misc TP1-1185 misc intelligent tractor misc vision navigation misc improved anti-noise morphology misc boundary line misc guided filtering misc Chemical technology
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title	Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology
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title_full	Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology
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author_browse	Wei Lu Mengjie Zeng Ling Wang Hui Luo Subrata Mukherjee Xuhui Huang Yiming Deng
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title_sort	navigation algorithm based on the boundary line of tillage soil combined with guided filtering and improved anti-noise morphology
callnumber	TP1-1185
title_auth	Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology
abstract	An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.094</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula< in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.113</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula<. The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<140</mn< <mtext< </mtext< <mo<×</mo< <mtext< </mtext< <mn<260</mn< </mrow< </semantics< </math< </inline-formula< pixels could achieve high-precision vision navigation while the course deviation angle was not more than <inline-formula< <math display="inline"< <semantics< <mrow< <mn<7.5</mn< <mo<°</mo< </mrow< </semantics< </math< </inline-formula<. The maximum tractor speed of the optimal template and global template were <inline-formula< <math display="inline"< <semantics< <mrow< <mn<51.41</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula< and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<27.47</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula<, respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.
abstractGer	An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.094</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula< in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.113</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula<. The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<140</mn< <mtext< </mtext< <mo<×</mo< <mtext< </mtext< <mn<260</mn< </mrow< </semantics< </math< </inline-formula< pixels could achieve high-precision vision navigation while the course deviation angle was not more than <inline-formula< <math display="inline"< <semantics< <mrow< <mn<7.5</mn< <mo<°</mo< </mrow< </semantics< </math< </inline-formula<. The maximum tractor speed of the optimal template and global template were <inline-formula< <math display="inline"< <semantics< <mrow< <mn<51.41</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula< and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<27.47</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula<, respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.
abstract_unstemmed	An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.094</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula< in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.113</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula<. The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<140</mn< <mtext< </mtext< <mo<×</mo< <mtext< </mtext< <mn<260</mn< </mrow< </semantics< </math< </inline-formula< pixels could achieve high-precision vision navigation while the course deviation angle was not more than <inline-formula< <math display="inline"< <semantics< <mrow< <mn<7.5</mn< <mo<°</mo< </mrow< </semantics< </math< </inline-formula<. The maximum tractor speed of the optimal template and global template were <inline-formula< <math display="inline"< <semantics< <mrow< <mn<51.41</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula< and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<27.47</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula<, respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.
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container_issue	18, p 3918
title_short	Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology
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At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.094</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula< in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0.113</mn< <mtext< </mtext< <mi mathvariant="normal"<s</mi< </mrow< </semantics< </math< </inline-formula<. The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<140</mn< <mtext< </mtext< <mo<×</mo< <mtext< </mtext< <mn<260</mn< </mrow< </semantics< </math< </inline-formula< pixels could achieve high-precision vision navigation while the course deviation angle was not more than <inline-formula< <math display="inline"< <semantics< <mrow< <mn<7.5</mn< <mo<°</mo< </mrow< </semantics< </math< </inline-formula<. The maximum tractor speed of the optimal template and global template were <inline-formula< <math display="inline"< <semantics< <mrow< <mn<51.41</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula< and <inline-formula< <math display="inline"< <semantics< <mrow< <mn<27.47</mn< <mrow< <mtext< </mtext< <mi<km</mi< </mrow< <mo</</mo< <mi mathvariant="normal"<h</mi< </mrow< </semantics< </math< </inline-formula<, respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">intelligent tractor</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">vision navigation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">improved anti-noise morphology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">boundary line</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">guided filtering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Chemical technology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mengjie 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Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology

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