Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method
Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clus...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ma, Zenghong [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
|---|
| Schlagwörter: |
Dynamic threshold segmentation |
|---|
| Umfang: |
14 |
|---|
| Übergeordnetes Werk: |
Enthalten in: The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) - Raggi, M. ELSEVIER, 2015, San Diego, Calif |
|---|---|
| Übergeordnetes Werk: |
volume:211 ; year:2021 ; pages:63-76 ; extent:14 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.biosystemseng.2021.08.030 |
|---|
| Katalog-ID: |
ELV055608965 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV055608965 | ||
| 003 | DE-627 | ||
| 005 | 20230626041907.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220105s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.biosystemseng.2021.08.030 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001599.pica |
| 035 | |a (DE-627)ELV055608965 | ||
| 035 | |a (ELSEVIER)S1537-5110(21)00214-2 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 630 |q VZ |
| 082 | 0 | 4 | |a 640 |q VZ |
| 082 | 0 | 4 | |a 320 |q VZ |
| 082 | 0 | 4 | |a 630 |a 640 |a 610 |q VZ |
| 100 | 1 | |a Ma, Zenghong |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method |
| 264 | 1 | |c 2021transfer abstract | |
| 300 | |a 14 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. | ||
| 520 | |a Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. | ||
| 650 | 7 | |a Outlier line detection |2 Elsevier | |
| 650 | 7 | |a Supervised learning |2 Elsevier | |
| 650 | 7 | |a Dynamic threshold segmentation |2 Elsevier | |
| 650 | 7 | |a Straight-line clustering algorithm |2 Elsevier | |
| 650 | 7 | |a Crop root row detection |2 Elsevier | |
| 700 | 1 | |a Tao, Zeyi |4 oth | |
| 700 | 1 | |a Du, Xiaoqiang |4 oth | |
| 700 | 1 | |a Yu, Yaxin |4 oth | |
| 700 | 1 | |a Wu, Chanyu |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Academ. Press |a Raggi, M. ELSEVIER |t The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) |d 2015 |g San Diego, Calif |w (DE-627)ELV018374581 |
| 773 | 1 | 8 | |g volume:211 |g year:2021 |g pages:63-76 |g extent:14 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.biosystemseng.2021.08.030 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_30 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_65 | ||
| 912 | |a GBV_ILN_70 | ||
| 951 | |a AR | ||
| 952 | |d 211 |j 2021 |h 63-76 |g 14 | ||
| author_variant |
z m zm |
|---|---|
| matchkey_str |
mazenghongtaozeyiduxiaoqiangyuyaxinwucha:2021----:uoaidtcinfrpotosnadfedbsdntagtielseigloi |
| hierarchy_sort_str |
2021transfer abstract |
| publishDate |
2021 |
| allfields |
10.1016/j.biosystemseng.2021.08.030 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001599.pica (DE-627)ELV055608965 (ELSEVIER)S1537-5110(21)00214-2 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 320 VZ 630 640 610 VZ Ma, Zenghong verfasserin aut Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method 2021transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. Outlier line detection Elsevier Supervised learning Elsevier Dynamic threshold segmentation Elsevier Straight-line clustering algorithm Elsevier Crop root row detection Elsevier Tao, Zeyi oth Du, Xiaoqiang oth Yu, Yaxin oth Wu, Chanyu oth Enthalten in Academ. Press Raggi, M. ELSEVIER The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) 2015 San Diego, Calif (DE-627)ELV018374581 volume:211 year:2021 pages:63-76 extent:14 https://doi.org/10.1016/j.biosystemseng.2021.08.030 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 AR 211 2021 63-76 14 |
| spelling |
10.1016/j.biosystemseng.2021.08.030 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001599.pica (DE-627)ELV055608965 (ELSEVIER)S1537-5110(21)00214-2 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 320 VZ 630 640 610 VZ Ma, Zenghong verfasserin aut Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method 2021transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. Outlier line detection Elsevier Supervised learning Elsevier Dynamic threshold segmentation Elsevier Straight-line clustering algorithm Elsevier Crop root row detection Elsevier Tao, Zeyi oth Du, Xiaoqiang oth Yu, Yaxin oth Wu, Chanyu oth Enthalten in Academ. Press Raggi, M. ELSEVIER The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) 2015 San Diego, Calif (DE-627)ELV018374581 volume:211 year:2021 pages:63-76 extent:14 https://doi.org/10.1016/j.biosystemseng.2021.08.030 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 AR 211 2021 63-76 14 |
| allfields_unstemmed |
10.1016/j.biosystemseng.2021.08.030 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001599.pica (DE-627)ELV055608965 (ELSEVIER)S1537-5110(21)00214-2 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 320 VZ 630 640 610 VZ Ma, Zenghong verfasserin aut Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method 2021transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. Outlier line detection Elsevier Supervised learning Elsevier Dynamic threshold segmentation Elsevier Straight-line clustering algorithm Elsevier Crop root row detection Elsevier Tao, Zeyi oth Du, Xiaoqiang oth Yu, Yaxin oth Wu, Chanyu oth Enthalten in Academ. Press Raggi, M. ELSEVIER The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) 2015 San Diego, Calif (DE-627)ELV018374581 volume:211 year:2021 pages:63-76 extent:14 https://doi.org/10.1016/j.biosystemseng.2021.08.030 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 AR 211 2021 63-76 14 |
| allfieldsGer |
10.1016/j.biosystemseng.2021.08.030 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001599.pica (DE-627)ELV055608965 (ELSEVIER)S1537-5110(21)00214-2 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 320 VZ 630 640 610 VZ Ma, Zenghong verfasserin aut Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method 2021transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. Outlier line detection Elsevier Supervised learning Elsevier Dynamic threshold segmentation Elsevier Straight-line clustering algorithm Elsevier Crop root row detection Elsevier Tao, Zeyi oth Du, Xiaoqiang oth Yu, Yaxin oth Wu, Chanyu oth Enthalten in Academ. Press Raggi, M. ELSEVIER The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) 2015 San Diego, Calif (DE-627)ELV018374581 volume:211 year:2021 pages:63-76 extent:14 https://doi.org/10.1016/j.biosystemseng.2021.08.030 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 AR 211 2021 63-76 14 |
| allfieldsSound |
10.1016/j.biosystemseng.2021.08.030 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001599.pica (DE-627)ELV055608965 (ELSEVIER)S1537-5110(21)00214-2 DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 320 VZ 630 640 610 VZ Ma, Zenghong verfasserin aut Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method 2021transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. Outlier line detection Elsevier Supervised learning Elsevier Dynamic threshold segmentation Elsevier Straight-line clustering algorithm Elsevier Crop root row detection Elsevier Tao, Zeyi oth Du, Xiaoqiang oth Yu, Yaxin oth Wu, Chanyu oth Enthalten in Academ. Press Raggi, M. ELSEVIER The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) 2015 San Diego, Calif (DE-627)ELV018374581 volume:211 year:2021 pages:63-76 extent:14 https://doi.org/10.1016/j.biosystemseng.2021.08.030 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 AR 211 2021 63-76 14 |
| language |
English |
| source |
Enthalten in The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) San Diego, Calif volume:211 year:2021 pages:63-76 extent:14 |
| sourceStr |
Enthalten in The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) San Diego, Calif volume:211 year:2021 pages:63-76 extent:14 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Outlier line detection Supervised learning Dynamic threshold segmentation Straight-line clustering algorithm Crop root row detection |
| dewey-raw |
630 |
| isfreeaccess_bool |
false |
| container_title |
The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) |
| authorswithroles_txt_mv |
Ma, Zenghong @@aut@@ Tao, Zeyi @@oth@@ Du, Xiaoqiang @@oth@@ Yu, Yaxin @@oth@@ Wu, Chanyu @@oth@@ |
| publishDateDaySort_date |
2021-01-01T00:00:00Z |
| hierarchy_top_id |
ELV018374581 |
| dewey-sort |
3630 |
| id |
ELV055608965 |
| 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">ELV055608965</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626041907.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.biosystemseng.2021.08.030</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001599.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV055608965</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1537-5110(21)00214-2</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="082" ind1="0" ind2="4"><subfield code="a">630</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">640</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">320</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">630</subfield><subfield code="a">640</subfield><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ma, Zenghong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">14</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Outlier line detection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Supervised learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Dynamic threshold segmentation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Straight-line clustering algorithm</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Crop root row detection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tao, Zeyi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Xiaoqiang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Yaxin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Chanyu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academ. Press</subfield><subfield code="a">Raggi, M. ELSEVIER</subfield><subfield code="t">The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy)</subfield><subfield code="d">2015</subfield><subfield code="g">San Diego, Calif</subfield><subfield code="w">(DE-627)ELV018374581</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:211</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:63-76</subfield><subfield code="g">extent:14</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.biosystemseng.2021.08.030</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_30</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">211</subfield><subfield code="j">2021</subfield><subfield code="h">63-76</subfield><subfield code="g">14</subfield></datafield></record></collection>
|
| author |
Ma, Zenghong |
| spellingShingle |
Ma, Zenghong ddc 630 ddc 640 ddc 320 Elsevier Outlier line detection Elsevier Supervised learning Elsevier Dynamic threshold segmentation Elsevier Straight-line clustering algorithm Elsevier Crop root row detection Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method |
| authorStr |
Ma, Zenghong |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV018374581 |
| format |
electronic Article |
| dewey-ones |
630 - Agriculture & related technologies 640 - Home & family management 320 - Political science 610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
630 VZ 640 VZ 320 VZ 630 640 610 VZ Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method Outlier line detection Elsevier Supervised learning Elsevier Dynamic threshold segmentation Elsevier Straight-line clustering algorithm Elsevier Crop root row detection Elsevier |
| topic |
ddc 630 ddc 640 ddc 320 Elsevier Outlier line detection Elsevier Supervised learning Elsevier Dynamic threshold segmentation Elsevier Straight-line clustering algorithm Elsevier Crop root row detection |
| topic_unstemmed |
ddc 630 ddc 640 ddc 320 Elsevier Outlier line detection Elsevier Supervised learning Elsevier Dynamic threshold segmentation Elsevier Straight-line clustering algorithm Elsevier Crop root row detection |
| topic_browse |
ddc 630 ddc 640 ddc 320 Elsevier Outlier line detection Elsevier Supervised learning Elsevier Dynamic threshold segmentation Elsevier Straight-line clustering algorithm Elsevier Crop root row detection |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
z t zt x d xd y y yy c w cw |
| hierarchy_parent_title |
The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) |
| hierarchy_parent_id |
ELV018374581 |
| dewey-tens |
630 - Agriculture 640 - Home & family management 320 - Political science 610 - Medicine & health |
| hierarchy_top_title |
The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV018374581 |
| title |
Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method |
| ctrlnum |
(DE-627)ELV055608965 (ELSEVIER)S1537-5110(21)00214-2 |
| title_full |
Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method |
| author_sort |
Ma, Zenghong |
| journal |
The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) |
| journalStr |
The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy) |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology 300 - Social sciences |
| recordtype |
marc |
| publishDateSort |
2021 |
| contenttype_str_mv |
zzz |
| container_start_page |
63 |
| author_browse |
Ma, Zenghong |
| container_volume |
211 |
| physical |
14 |
| class |
630 VZ 640 VZ 320 VZ 630 640 610 VZ |
| format_se |
Elektronische Aufsätze |
| author-letter |
Ma, Zenghong |
| doi_str_mv |
10.1016/j.biosystemseng.2021.08.030 |
| dewey-full |
630 640 320 610 |
| title_sort |
automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method |
| title_auth |
Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method |
| abstract |
Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. |
| abstractGer |
Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. |
| abstract_unstemmed |
Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_30 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 |
| title_short |
Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method |
| url |
https://doi.org/10.1016/j.biosystemseng.2021.08.030 |
| remote_bool |
true |
| author2 |
Tao, Zeyi Du, Xiaoqiang Yu, Yaxin Wu, Chanyu |
| author2Str |
Tao, Zeyi Du, Xiaoqiang Yu, Yaxin Wu, Chanyu |
| ppnlink |
ELV018374581 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth |
| doi_str |
10.1016/j.biosystemseng.2021.08.030 |
| up_date |
2024-07-06T18:01:22.406Z |
| _version_ |
1803853635158802432 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV055608965</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626041907.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.biosystemseng.2021.08.030</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001599.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV055608965</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1537-5110(21)00214-2</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="082" ind1="0" ind2="4"><subfield code="a">630</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">640</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">320</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">630</subfield><subfield code="a">640</subfield><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ma, Zenghong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">14</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Crop root row detection is critical information for a robot working in a paddy field. When crop root row information in an image is obtained through machine vision, it is easily disturbed by natural light, weeds, and crop growth. A robust crop root row detection algorithm based on straight-line clustering and supervised learning was proposed. First, a combination of vegetation indices and dynamic threshold segmentation was used to binarise the rice images. Second, the number of clusters of crop rows in the image was obtained by the horizontal strips method at the top of the image. Third, crop rows were acquired by a straight-line clustering algorithm, and crop rows on both sides of the image were deleted through the outlier line detection mechanism. Fourth, the parametric regression equation between crop rows and crop root rows was calculated by supervised learning. The experimental results show that the accuracy of crop root row detection was 96.79%, 90.82%, and 84.15% respectively for the 6th, 20th, and 35th days after transplantation.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Outlier line detection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Supervised learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Dynamic threshold segmentation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Straight-line clustering algorithm</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Crop root row detection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tao, Zeyi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Xiaoqiang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Yaxin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Chanyu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academ. Press</subfield><subfield code="a">Raggi, M. ELSEVIER</subfield><subfield code="t">The role of policy priorities and targeting in the spatial location of participation in Agri-Environmental Schemes in Emilia-Romagna (Italy)</subfield><subfield code="d">2015</subfield><subfield code="g">San Diego, Calif</subfield><subfield code="w">(DE-627)ELV018374581</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:211</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:63-76</subfield><subfield code="g">extent:14</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.biosystemseng.2021.08.030</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_30</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">211</subfield><subfield code="j">2021</subfield><subfield code="h">63-76</subfield><subfield code="g">14</subfield></datafield></record></collection>
|
| score |
7.3993263 |