Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR

As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine...
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Gespeichert in:

Autor*in:	Shaolin Zhang [verfasserIn] Qianglong Ma [verfasserIn] Shangkun Cheng [verfasserIn] Dong An [verfasserIn] Zhenling Yang [verfasserIn] Biao Ma [verfasserIn] Yang Yang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	LiDAR crop row detection K-means horizontal strips

Übergeordnetes Werk:	In: Agriculture - MDPI AG, 2012, 12(2022), 12, p 2011
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:12, p 2011

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/agriculture12122011

Katalog-ID:	DOAJ083250573

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allfields	10.3390/agriculture12122011 doi (DE-627)DOAJ083250573 (DE-599)DOAJdd6d3f8e11c14119b829b29d6dcf531b DE-627 ger DE-627 rakwb eng S1-972 Shaolin Zhang verfasserin aut Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture. LiDAR crop row detection K-means horizontal strips Agriculture (General) Qianglong Ma verfasserin aut Shangkun Cheng verfasserin aut Dong An verfasserin aut Zhenling Yang verfasserin aut Biao Ma verfasserin aut Yang Yang verfasserin aut In Agriculture MDPI AG, 2012 12(2022), 12, p 2011 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:12 year:2022 number:12, p 2011 https://doi.org/10.3390/agriculture12122011 kostenfrei https://doaj.org/article/dd6d3f8e11c14119b829b29d6dcf531b kostenfrei https://www.mdpi.com/2077-0472/12/12/2011 kostenfrei https://doaj.org/toc/2077-0472 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_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 12 2022 12, p 2011
spelling	10.3390/agriculture12122011 doi (DE-627)DOAJ083250573 (DE-599)DOAJdd6d3f8e11c14119b829b29d6dcf531b DE-627 ger DE-627 rakwb eng S1-972 Shaolin Zhang verfasserin aut Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture. LiDAR crop row detection K-means horizontal strips Agriculture (General) Qianglong Ma verfasserin aut Shangkun Cheng verfasserin aut Dong An verfasserin aut Zhenling Yang verfasserin aut Biao Ma verfasserin aut Yang Yang verfasserin aut In Agriculture MDPI AG, 2012 12(2022), 12, p 2011 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:12 year:2022 number:12, p 2011 https://doi.org/10.3390/agriculture12122011 kostenfrei https://doaj.org/article/dd6d3f8e11c14119b829b29d6dcf531b kostenfrei https://www.mdpi.com/2077-0472/12/12/2011 kostenfrei https://doaj.org/toc/2077-0472 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_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 12 2022 12, p 2011
allfields_unstemmed	10.3390/agriculture12122011 doi (DE-627)DOAJ083250573 (DE-599)DOAJdd6d3f8e11c14119b829b29d6dcf531b DE-627 ger DE-627 rakwb eng S1-972 Shaolin Zhang verfasserin aut Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture. LiDAR crop row detection K-means horizontal strips Agriculture (General) Qianglong Ma verfasserin aut Shangkun Cheng verfasserin aut Dong An verfasserin aut Zhenling Yang verfasserin aut Biao Ma verfasserin aut Yang Yang verfasserin aut In Agriculture MDPI AG, 2012 12(2022), 12, p 2011 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:12 year:2022 number:12, p 2011 https://doi.org/10.3390/agriculture12122011 kostenfrei https://doaj.org/article/dd6d3f8e11c14119b829b29d6dcf531b kostenfrei https://www.mdpi.com/2077-0472/12/12/2011 kostenfrei https://doaj.org/toc/2077-0472 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_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 12 2022 12, p 2011
allfieldsGer	10.3390/agriculture12122011 doi (DE-627)DOAJ083250573 (DE-599)DOAJdd6d3f8e11c14119b829b29d6dcf531b DE-627 ger DE-627 rakwb eng S1-972 Shaolin Zhang verfasserin aut Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture. LiDAR crop row detection K-means horizontal strips Agriculture (General) Qianglong Ma verfasserin aut Shangkun Cheng verfasserin aut Dong An verfasserin aut Zhenling Yang verfasserin aut Biao Ma verfasserin aut Yang Yang verfasserin aut In Agriculture MDPI AG, 2012 12(2022), 12, p 2011 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:12 year:2022 number:12, p 2011 https://doi.org/10.3390/agriculture12122011 kostenfrei https://doaj.org/article/dd6d3f8e11c14119b829b29d6dcf531b kostenfrei https://www.mdpi.com/2077-0472/12/12/2011 kostenfrei https://doaj.org/toc/2077-0472 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_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 12 2022 12, p 2011
allfieldsSound	10.3390/agriculture12122011 doi (DE-627)DOAJ083250573 (DE-599)DOAJdd6d3f8e11c14119b829b29d6dcf531b DE-627 ger DE-627 rakwb eng S1-972 Shaolin Zhang verfasserin aut Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture. LiDAR crop row detection K-means horizontal strips Agriculture (General) Qianglong Ma verfasserin aut Shangkun Cheng verfasserin aut Dong An verfasserin aut Zhenling Yang verfasserin aut Biao Ma verfasserin aut Yang Yang verfasserin aut In Agriculture MDPI AG, 2012 12(2022), 12, p 2011 (DE-627)686948173 (DE-600)2651678-0 20770472 nnns volume:12 year:2022 number:12, p 2011 https://doi.org/10.3390/agriculture12122011 kostenfrei https://doaj.org/article/dd6d3f8e11c14119b829b29d6dcf531b kostenfrei https://www.mdpi.com/2077-0472/12/12/2011 kostenfrei https://doaj.org/toc/2077-0472 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_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 12 2022 12, p 2011
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authorswithroles_txt_mv	Shaolin Zhang @@aut@@ Qianglong Ma @@aut@@ Shangkun Cheng @@aut@@ Dong An @@aut@@ Zhenling Yang @@aut@@ Biao Ma @@aut@@ Yang Yang @@aut@@
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Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. 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callnumber-first	S - Agriculture
author	Shaolin Zhang
spellingShingle	Shaolin Zhang misc S1-972 misc LiDAR misc crop row detection misc K-means misc horizontal strips misc Agriculture (General) Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR
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topic_title	S1-972 Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR LiDAR crop row detection K-means horizontal strips
topic	misc S1-972 misc LiDAR misc crop row detection misc K-means misc horizontal strips misc Agriculture (General)
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title	Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR
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title_full	Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR
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author_browse	Shaolin Zhang Qianglong Ma Shangkun Cheng Dong An Zhenling Yang Biao Ma Yang Yang
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title_sort	crop row detection in the middle and late periods of maize under sheltering based on solid state lidar
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title_auth	Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR
abstract	As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture.
abstractGer	As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture.
abstract_unstemmed	As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture.
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container_issue	12, p 2011
title_short	Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR
url	https://doi.org/10.3390/agriculture12122011 https://doaj.org/article/dd6d3f8e11c14119b829b29d6dcf531b https://www.mdpi.com/2077-0472/12/12/2011 https://doaj.org/toc/2077-0472
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author2	Qianglong Ma Shangkun Cheng Dong An Zhenling Yang Biao Ma Yang Yang
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up_date	2024-07-03T16:25:25.807Z
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Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR

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