Automated detection of Crop-Row lines and measurement of maize width for boom spraying
Crop-row line detection is key to realizing the accurate spraying of crops; however, it is challenging because of uneven lighting, weeds, and missing plants within rows. In this study, we propose an automatic detection method for maize width and row centerlines based on image processing that can det...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Xinyue [verfasserIn] Wang, Qingjie [verfasserIn] Wang, Xiuhong [verfasserIn] Li, Hongwen [verfasserIn] He, Jin [verfasserIn] Lu, Caiyun [verfasserIn] Yang, Yang [verfasserIn] Jiang, Shan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Computers and electronics in agriculture - Amsterdam [u.a.] : Elsevier Science, 1985, 215 |
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| Übergeordnetes Werk: |
volume:215 |
| DOI / URN: |
10.1016/j.compag.2023.108406 |
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| Katalog-ID: |
ELV066021502 |
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| 520 | |a Crop-row line detection is key to realizing the accurate spraying of crops; however, it is challenging because of uneven lighting, weeds, and missing plants within rows. In this study, we propose an automatic detection method for maize width and row centerlines based on image processing that can detect maize at various growth stages and under complex field conditions. Our method consists of three stages: (i) Maize crop-row segmentation based on ResC-UNet, an improved version of U-Net that integrates ResNet-50 and attention mechanism modules. (ii) Correction of distorted images by adaptive perspective transformation to prevent parallel crop rows from creating “vanishing points” at the top of the image. (iii) Detection of the width and missing plants in the row through width fitting, and extraction of the actual row line according to the positional relationship between the width and row line. We established a dataset of maize crop rows by capturing images of maize at different growth stages in the field. Offline experiments on this dataset showed that our method achieved 94.9% accuracy for maize row segmentation, 1.71° average error angle for row-line fitting, and 91.51% average pixel accuracy for width detection. It thus enables technical support for precise variable-rate spraying along rows during the maize seedling stage. | ||
| 650 | 4 | |a Crop row line detection | |
| 650 | 4 | |a Semantic segmentation | |
| 650 | 4 | |a Crop row width detection | |
| 650 | 4 | |a Crop row missing plant detection | |
| 650 | 4 | |a Adaptive perspective transformation | |
| 700 | 1 | |a Wang, Qingjie |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiuhong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Hongwen |e verfasserin |4 aut | |
| 700 | 1 | |a He, Jin |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Caiyun |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Shan |e verfasserin |4 aut | |
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10.1016/j.compag.2023.108406 doi (DE-627)ELV066021502 (ELSEVIER)S0168-1699(23)00794-9 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Zhang, Xinyue verfasserin aut Automated detection of Crop-Row lines and measurement of maize width for boom spraying 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop-row line detection is key to realizing the accurate spraying of crops; however, it is challenging because of uneven lighting, weeds, and missing plants within rows. In this study, we propose an automatic detection method for maize width and row centerlines based on image processing that can detect maize at various growth stages and under complex field conditions. Our method consists of three stages: (i) Maize crop-row segmentation based on ResC-UNet, an improved version of U-Net that integrates ResNet-50 and attention mechanism modules. (ii) Correction of distorted images by adaptive perspective transformation to prevent parallel crop rows from creating “vanishing points” at the top of the image. (iii) Detection of the width and missing plants in the row through width fitting, and extraction of the actual row line according to the positional relationship between the width and row line. We established a dataset of maize crop rows by capturing images of maize at different growth stages in the field. Offline experiments on this dataset showed that our method achieved 94.9% accuracy for maize row segmentation, 1.71° average error angle for row-line fitting, and 91.51% average pixel accuracy for width detection. It thus enables technical support for precise variable-rate spraying along rows during the maize seedling stage. Crop row line detection Semantic segmentation Crop row width detection Crop row missing plant detection Adaptive perspective transformation Wang, Qingjie verfasserin aut Wang, Xiuhong verfasserin aut Li, Hongwen verfasserin aut He, Jin verfasserin aut Lu, Caiyun verfasserin aut Yang, Yang verfasserin aut Jiang, Shan verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 215 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:215 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 215 |
| spelling |
10.1016/j.compag.2023.108406 doi (DE-627)ELV066021502 (ELSEVIER)S0168-1699(23)00794-9 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Zhang, Xinyue verfasserin aut Automated detection of Crop-Row lines and measurement of maize width for boom spraying 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop-row line detection is key to realizing the accurate spraying of crops; however, it is challenging because of uneven lighting, weeds, and missing plants within rows. In this study, we propose an automatic detection method for maize width and row centerlines based on image processing that can detect maize at various growth stages and under complex field conditions. Our method consists of three stages: (i) Maize crop-row segmentation based on ResC-UNet, an improved version of U-Net that integrates ResNet-50 and attention mechanism modules. (ii) Correction of distorted images by adaptive perspective transformation to prevent parallel crop rows from creating “vanishing points” at the top of the image. (iii) Detection of the width and missing plants in the row through width fitting, and extraction of the actual row line according to the positional relationship between the width and row line. We established a dataset of maize crop rows by capturing images of maize at different growth stages in the field. Offline experiments on this dataset showed that our method achieved 94.9% accuracy for maize row segmentation, 1.71° average error angle for row-line fitting, and 91.51% average pixel accuracy for width detection. It thus enables technical support for precise variable-rate spraying along rows during the maize seedling stage. Crop row line detection Semantic segmentation Crop row width detection Crop row missing plant detection Adaptive perspective transformation Wang, Qingjie verfasserin aut Wang, Xiuhong verfasserin aut Li, Hongwen verfasserin aut He, Jin verfasserin aut Lu, Caiyun verfasserin aut Yang, Yang verfasserin aut Jiang, Shan verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 215 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:215 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 215 |
| allfields_unstemmed |
10.1016/j.compag.2023.108406 doi (DE-627)ELV066021502 (ELSEVIER)S0168-1699(23)00794-9 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Zhang, Xinyue verfasserin aut Automated detection of Crop-Row lines and measurement of maize width for boom spraying 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop-row line detection is key to realizing the accurate spraying of crops; however, it is challenging because of uneven lighting, weeds, and missing plants within rows. In this study, we propose an automatic detection method for maize width and row centerlines based on image processing that can detect maize at various growth stages and under complex field conditions. Our method consists of three stages: (i) Maize crop-row segmentation based on ResC-UNet, an improved version of U-Net that integrates ResNet-50 and attention mechanism modules. (ii) Correction of distorted images by adaptive perspective transformation to prevent parallel crop rows from creating “vanishing points” at the top of the image. (iii) Detection of the width and missing plants in the row through width fitting, and extraction of the actual row line according to the positional relationship between the width and row line. We established a dataset of maize crop rows by capturing images of maize at different growth stages in the field. Offline experiments on this dataset showed that our method achieved 94.9% accuracy for maize row segmentation, 1.71° average error angle for row-line fitting, and 91.51% average pixel accuracy for width detection. It thus enables technical support for precise variable-rate spraying along rows during the maize seedling stage. Crop row line detection Semantic segmentation Crop row width detection Crop row missing plant detection Adaptive perspective transformation Wang, Qingjie verfasserin aut Wang, Xiuhong verfasserin aut Li, Hongwen verfasserin aut He, Jin verfasserin aut Lu, Caiyun verfasserin aut Yang, Yang verfasserin aut Jiang, Shan verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 215 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:215 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 215 |
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10.1016/j.compag.2023.108406 doi (DE-627)ELV066021502 (ELSEVIER)S0168-1699(23)00794-9 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Zhang, Xinyue verfasserin aut Automated detection of Crop-Row lines and measurement of maize width for boom spraying 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop-row line detection is key to realizing the accurate spraying of crops; however, it is challenging because of uneven lighting, weeds, and missing plants within rows. In this study, we propose an automatic detection method for maize width and row centerlines based on image processing that can detect maize at various growth stages and under complex field conditions. Our method consists of three stages: (i) Maize crop-row segmentation based on ResC-UNet, an improved version of U-Net that integrates ResNet-50 and attention mechanism modules. (ii) Correction of distorted images by adaptive perspective transformation to prevent parallel crop rows from creating “vanishing points” at the top of the image. (iii) Detection of the width and missing plants in the row through width fitting, and extraction of the actual row line according to the positional relationship between the width and row line. We established a dataset of maize crop rows by capturing images of maize at different growth stages in the field. Offline experiments on this dataset showed that our method achieved 94.9% accuracy for maize row segmentation, 1.71° average error angle for row-line fitting, and 91.51% average pixel accuracy for width detection. It thus enables technical support for precise variable-rate spraying along rows during the maize seedling stage. Crop row line detection Semantic segmentation Crop row width detection Crop row missing plant detection Adaptive perspective transformation Wang, Qingjie verfasserin aut Wang, Xiuhong verfasserin aut Li, Hongwen verfasserin aut He, Jin verfasserin aut Lu, Caiyun verfasserin aut Yang, Yang verfasserin aut Jiang, Shan verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 215 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:215 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 215 |
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10.1016/j.compag.2023.108406 doi (DE-627)ELV066021502 (ELSEVIER)S0168-1699(23)00794-9 DE-627 ger DE-627 rda eng 620 630 640 004 VZ 48.03 bkl Zhang, Xinyue verfasserin aut Automated detection of Crop-Row lines and measurement of maize width for boom spraying 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Crop-row line detection is key to realizing the accurate spraying of crops; however, it is challenging because of uneven lighting, weeds, and missing plants within rows. In this study, we propose an automatic detection method for maize width and row centerlines based on image processing that can detect maize at various growth stages and under complex field conditions. Our method consists of three stages: (i) Maize crop-row segmentation based on ResC-UNet, an improved version of U-Net that integrates ResNet-50 and attention mechanism modules. (ii) Correction of distorted images by adaptive perspective transformation to prevent parallel crop rows from creating “vanishing points” at the top of the image. (iii) Detection of the width and missing plants in the row through width fitting, and extraction of the actual row line according to the positional relationship between the width and row line. We established a dataset of maize crop rows by capturing images of maize at different growth stages in the field. Offline experiments on this dataset showed that our method achieved 94.9% accuracy for maize row segmentation, 1.71° average error angle for row-line fitting, and 91.51% average pixel accuracy for width detection. It thus enables technical support for precise variable-rate spraying along rows during the maize seedling stage. Crop row line detection Semantic segmentation Crop row width detection Crop row missing plant detection Adaptive perspective transformation Wang, Qingjie verfasserin aut Wang, Xiuhong verfasserin aut Li, Hongwen verfasserin aut He, Jin verfasserin aut Lu, Caiyun verfasserin aut Yang, Yang verfasserin aut Jiang, Shan verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 215 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:215 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.03 Methoden und Techniken der Land- und Forstwirtschaft VZ AR 215 |
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Methoden und Techniken der Land- und Forstwirtschaft |
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Crop row line detection Semantic segmentation Crop row width detection Crop row missing plant detection Adaptive perspective transformation |
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Zhang, Xinyue @@aut@@ Wang, Qingjie @@aut@@ Wang, Xiuhong @@aut@@ Li, Hongwen @@aut@@ He, Jin @@aut@@ Lu, Caiyun @@aut@@ Yang, Yang @@aut@@ Jiang, Shan @@aut@@ |
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Zhang, Xinyue ddc 620 bkl 48.03 misc Crop row line detection misc Semantic segmentation misc Crop row width detection misc Crop row missing plant detection misc Adaptive perspective transformation Automated detection of Crop-Row lines and measurement of maize width for boom spraying |
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620 630 640 004 VZ 48.03 bkl Automated detection of Crop-Row lines and measurement of maize width for boom spraying Crop row line detection Semantic segmentation Crop row width detection Crop row missing plant detection Adaptive perspective transformation |
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Automated detection of Crop-Row lines and measurement of maize width for boom spraying |
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Zhang, Xinyue Wang, Qingjie Wang, Xiuhong Li, Hongwen He, Jin Lu, Caiyun Yang, Yang Jiang, Shan |
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automated detection of crop-row lines and measurement of maize width for boom spraying |
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Automated detection of Crop-Row lines and measurement of maize width for boom spraying |
| abstract |
Crop-row line detection is key to realizing the accurate spraying of crops; however, it is challenging because of uneven lighting, weeds, and missing plants within rows. In this study, we propose an automatic detection method for maize width and row centerlines based on image processing that can detect maize at various growth stages and under complex field conditions. Our method consists of three stages: (i) Maize crop-row segmentation based on ResC-UNet, an improved version of U-Net that integrates ResNet-50 and attention mechanism modules. (ii) Correction of distorted images by adaptive perspective transformation to prevent parallel crop rows from creating “vanishing points” at the top of the image. (iii) Detection of the width and missing plants in the row through width fitting, and extraction of the actual row line according to the positional relationship between the width and row line. We established a dataset of maize crop rows by capturing images of maize at different growth stages in the field. Offline experiments on this dataset showed that our method achieved 94.9% accuracy for maize row segmentation, 1.71° average error angle for row-line fitting, and 91.51% average pixel accuracy for width detection. It thus enables technical support for precise variable-rate spraying along rows during the maize seedling stage. |
| abstractGer |
Crop-row line detection is key to realizing the accurate spraying of crops; however, it is challenging because of uneven lighting, weeds, and missing plants within rows. In this study, we propose an automatic detection method for maize width and row centerlines based on image processing that can detect maize at various growth stages and under complex field conditions. Our method consists of three stages: (i) Maize crop-row segmentation based on ResC-UNet, an improved version of U-Net that integrates ResNet-50 and attention mechanism modules. (ii) Correction of distorted images by adaptive perspective transformation to prevent parallel crop rows from creating “vanishing points” at the top of the image. (iii) Detection of the width and missing plants in the row through width fitting, and extraction of the actual row line according to the positional relationship between the width and row line. We established a dataset of maize crop rows by capturing images of maize at different growth stages in the field. Offline experiments on this dataset showed that our method achieved 94.9% accuracy for maize row segmentation, 1.71° average error angle for row-line fitting, and 91.51% average pixel accuracy for width detection. It thus enables technical support for precise variable-rate spraying along rows during the maize seedling stage. |
| abstract_unstemmed |
Crop-row line detection is key to realizing the accurate spraying of crops; however, it is challenging because of uneven lighting, weeds, and missing plants within rows. In this study, we propose an automatic detection method for maize width and row centerlines based on image processing that can detect maize at various growth stages and under complex field conditions. Our method consists of three stages: (i) Maize crop-row segmentation based on ResC-UNet, an improved version of U-Net that integrates ResNet-50 and attention mechanism modules. (ii) Correction of distorted images by adaptive perspective transformation to prevent parallel crop rows from creating “vanishing points” at the top of the image. (iii) Detection of the width and missing plants in the row through width fitting, and extraction of the actual row line according to the positional relationship between the width and row line. We established a dataset of maize crop rows by capturing images of maize at different growth stages in the field. Offline experiments on this dataset showed that our method achieved 94.9% accuracy for maize row segmentation, 1.71° average error angle for row-line fitting, and 91.51% average pixel accuracy for width detection. It thus enables technical support for precise variable-rate spraying along rows during the maize seedling stage. |
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Automated detection of Crop-Row lines and measurement of maize width for boom spraying |
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Wang, Qingjie Wang, Xiuhong Li, Hongwen He, Jin Lu, Caiyun Yang, Yang Jiang, Shan |
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| score |
7.4011183 |