QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions

Abstract Crop population and inter-plant spacing in corn farms can provide useful insight into plant phenotypic analysis and informed establishment decisions, improving crop productivity. Traditionally, farmers have relied on manual inspection to assess crop consistency, such as counting plant stand...
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Autor*in:	Islam, Fatimah [verfasserIn] Ullah, Muhib Bais, Abdul

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Plant spacing statistics Crop row detection Progressive probabilistic Hough transform Perspective transformation

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Neural computing & applications - London : Springer, 1993, 35(2023), 35 vom: 14. Sept., Seite 24877-24896
Übergeordnetes Werk:	volume:35 ; year:2023 ; number:35 ; day:14 ; month:09 ; pages:24877-24896

Links:	Volltext

DOI / URN:	10.1007/s00521-023-08961-8

Katalog-ID:	SPR053758862

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allfields	10.1007/s00521-023-08961-8 doi (DE-627)SPR053758862 (SPR)s00521-023-08961-8-e DE-627 ger DE-627 rakwb eng Islam, Fatimah verfasserin aut QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Crop population and inter-plant spacing in corn farms can provide useful insight into plant phenotypic analysis and informed establishment decisions, improving crop productivity. Traditionally, farmers have relied on manual inspection to assess crop consistency, such as counting plant stands and manually estimating plant spacing. This assessment is carried out in small predefined areas, leading to insufficient crop consistency analysis on the entire field. Moreover, alternative computer vision techniques computing only one or two key parameters also prove insufficient for accurate crop consistency assessment. This research presents a framework called QuanCro that utilizes red–green–blue images from field machinery to analyze crop parameters such as plant stands counting, plant emergence rate, and plant spacing. It utilizes the state-of-the-art object detection network—You Only Look Once version 7 (YOLOv7), to locate and count corn plants combined with our proposed semantic segmentation model, Small Pyramid-UNet (SP-UNet) architecture, to determine leaf area index. This architecture is designed to be memory efficient and computationally less expensive than similar networks, such as HRNet_Mscale (72.1M) and SegNet (34.65M), as it has approximately 21M parameters. The SP-UNet is further integrated with the Zhang–Suen thinning technique and progressive probabilistic Hough transform for crop row detection and plant spacing information. QuanCro accurately estimates crop densities and identifies inconsistent crop areas. The method is tested using 8000 images and shows a mean average precision of 0.976 for identifying plant stands. The SP-UNet achieves intersection over union scores of 0.973, 0.924, and 0.926 for crops, rows, and backgrounds, respectively. Plant spacing statistics (dpeaa)DE-He213 Crop row detection (dpeaa)DE-He213 Progressive probabilistic Hough transform (dpeaa)DE-He213 Perspective transformation (dpeaa)DE-He213 Ullah, Muhib (orcid)0000-0003-4943-2405 aut Bais, Abdul aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 35 vom: 14. Sept., Seite 24877-24896 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:35 day:14 month:09 pages:24877-24896 https://dx.doi.org/10.1007/s00521-023-08961-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 35 14 09 24877-24896
spelling	10.1007/s00521-023-08961-8 doi (DE-627)SPR053758862 (SPR)s00521-023-08961-8-e DE-627 ger DE-627 rakwb eng Islam, Fatimah verfasserin aut QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Crop population and inter-plant spacing in corn farms can provide useful insight into plant phenotypic analysis and informed establishment decisions, improving crop productivity. Traditionally, farmers have relied on manual inspection to assess crop consistency, such as counting plant stands and manually estimating plant spacing. This assessment is carried out in small predefined areas, leading to insufficient crop consistency analysis on the entire field. Moreover, alternative computer vision techniques computing only one or two key parameters also prove insufficient for accurate crop consistency assessment. This research presents a framework called QuanCro that utilizes red–green–blue images from field machinery to analyze crop parameters such as plant stands counting, plant emergence rate, and plant spacing. It utilizes the state-of-the-art object detection network—You Only Look Once version 7 (YOLOv7), to locate and count corn plants combined with our proposed semantic segmentation model, Small Pyramid-UNet (SP-UNet) architecture, to determine leaf area index. This architecture is designed to be memory efficient and computationally less expensive than similar networks, such as HRNet_Mscale (72.1M) and SegNet (34.65M), as it has approximately 21M parameters. The SP-UNet is further integrated with the Zhang–Suen thinning technique and progressive probabilistic Hough transform for crop row detection and plant spacing information. QuanCro accurately estimates crop densities and identifies inconsistent crop areas. The method is tested using 8000 images and shows a mean average precision of 0.976 for identifying plant stands. The SP-UNet achieves intersection over union scores of 0.973, 0.924, and 0.926 for crops, rows, and backgrounds, respectively. Plant spacing statistics (dpeaa)DE-He213 Crop row detection (dpeaa)DE-He213 Progressive probabilistic Hough transform (dpeaa)DE-He213 Perspective transformation (dpeaa)DE-He213 Ullah, Muhib (orcid)0000-0003-4943-2405 aut Bais, Abdul aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 35 vom: 14. Sept., Seite 24877-24896 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:35 day:14 month:09 pages:24877-24896 https://dx.doi.org/10.1007/s00521-023-08961-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 35 14 09 24877-24896
allfields_unstemmed	10.1007/s00521-023-08961-8 doi (DE-627)SPR053758862 (SPR)s00521-023-08961-8-e DE-627 ger DE-627 rakwb eng Islam, Fatimah verfasserin aut QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Crop population and inter-plant spacing in corn farms can provide useful insight into plant phenotypic analysis and informed establishment decisions, improving crop productivity. Traditionally, farmers have relied on manual inspection to assess crop consistency, such as counting plant stands and manually estimating plant spacing. This assessment is carried out in small predefined areas, leading to insufficient crop consistency analysis on the entire field. Moreover, alternative computer vision techniques computing only one or two key parameters also prove insufficient for accurate crop consistency assessment. This research presents a framework called QuanCro that utilizes red–green–blue images from field machinery to analyze crop parameters such as plant stands counting, plant emergence rate, and plant spacing. It utilizes the state-of-the-art object detection network—You Only Look Once version 7 (YOLOv7), to locate and count corn plants combined with our proposed semantic segmentation model, Small Pyramid-UNet (SP-UNet) architecture, to determine leaf area index. This architecture is designed to be memory efficient and computationally less expensive than similar networks, such as HRNet_Mscale (72.1M) and SegNet (34.65M), as it has approximately 21M parameters. The SP-UNet is further integrated with the Zhang–Suen thinning technique and progressive probabilistic Hough transform for crop row detection and plant spacing information. QuanCro accurately estimates crop densities and identifies inconsistent crop areas. The method is tested using 8000 images and shows a mean average precision of 0.976 for identifying plant stands. The SP-UNet achieves intersection over union scores of 0.973, 0.924, and 0.926 for crops, rows, and backgrounds, respectively. Plant spacing statistics (dpeaa)DE-He213 Crop row detection (dpeaa)DE-He213 Progressive probabilistic Hough transform (dpeaa)DE-He213 Perspective transformation (dpeaa)DE-He213 Ullah, Muhib (orcid)0000-0003-4943-2405 aut Bais, Abdul aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 35 vom: 14. Sept., Seite 24877-24896 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:35 day:14 month:09 pages:24877-24896 https://dx.doi.org/10.1007/s00521-023-08961-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 35 14 09 24877-24896
allfieldsGer	10.1007/s00521-023-08961-8 doi (DE-627)SPR053758862 (SPR)s00521-023-08961-8-e DE-627 ger DE-627 rakwb eng Islam, Fatimah verfasserin aut QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Crop population and inter-plant spacing in corn farms can provide useful insight into plant phenotypic analysis and informed establishment decisions, improving crop productivity. Traditionally, farmers have relied on manual inspection to assess crop consistency, such as counting plant stands and manually estimating plant spacing. This assessment is carried out in small predefined areas, leading to insufficient crop consistency analysis on the entire field. Moreover, alternative computer vision techniques computing only one or two key parameters also prove insufficient for accurate crop consistency assessment. This research presents a framework called QuanCro that utilizes red–green–blue images from field machinery to analyze crop parameters such as plant stands counting, plant emergence rate, and plant spacing. It utilizes the state-of-the-art object detection network—You Only Look Once version 7 (YOLOv7), to locate and count corn plants combined with our proposed semantic segmentation model, Small Pyramid-UNet (SP-UNet) architecture, to determine leaf area index. This architecture is designed to be memory efficient and computationally less expensive than similar networks, such as HRNet_Mscale (72.1M) and SegNet (34.65M), as it has approximately 21M parameters. The SP-UNet is further integrated with the Zhang–Suen thinning technique and progressive probabilistic Hough transform for crop row detection and plant spacing information. QuanCro accurately estimates crop densities and identifies inconsistent crop areas. The method is tested using 8000 images and shows a mean average precision of 0.976 for identifying plant stands. The SP-UNet achieves intersection over union scores of 0.973, 0.924, and 0.926 for crops, rows, and backgrounds, respectively. Plant spacing statistics (dpeaa)DE-He213 Crop row detection (dpeaa)DE-He213 Progressive probabilistic Hough transform (dpeaa)DE-He213 Perspective transformation (dpeaa)DE-He213 Ullah, Muhib (orcid)0000-0003-4943-2405 aut Bais, Abdul aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 35 vom: 14. Sept., Seite 24877-24896 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:35 day:14 month:09 pages:24877-24896 https://dx.doi.org/10.1007/s00521-023-08961-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 35 14 09 24877-24896
allfieldsSound	10.1007/s00521-023-08961-8 doi (DE-627)SPR053758862 (SPR)s00521-023-08961-8-e DE-627 ger DE-627 rakwb eng Islam, Fatimah verfasserin aut QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Crop population and inter-plant spacing in corn farms can provide useful insight into plant phenotypic analysis and informed establishment decisions, improving crop productivity. Traditionally, farmers have relied on manual inspection to assess crop consistency, such as counting plant stands and manually estimating plant spacing. This assessment is carried out in small predefined areas, leading to insufficient crop consistency analysis on the entire field. Moreover, alternative computer vision techniques computing only one or two key parameters also prove insufficient for accurate crop consistency assessment. This research presents a framework called QuanCro that utilizes red–green–blue images from field machinery to analyze crop parameters such as plant stands counting, plant emergence rate, and plant spacing. It utilizes the state-of-the-art object detection network—You Only Look Once version 7 (YOLOv7), to locate and count corn plants combined with our proposed semantic segmentation model, Small Pyramid-UNet (SP-UNet) architecture, to determine leaf area index. This architecture is designed to be memory efficient and computationally less expensive than similar networks, such as HRNet_Mscale (72.1M) and SegNet (34.65M), as it has approximately 21M parameters. The SP-UNet is further integrated with the Zhang–Suen thinning technique and progressive probabilistic Hough transform for crop row detection and plant spacing information. QuanCro accurately estimates crop densities and identifies inconsistent crop areas. The method is tested using 8000 images and shows a mean average precision of 0.976 for identifying plant stands. The SP-UNet achieves intersection over union scores of 0.973, 0.924, and 0.926 for crops, rows, and backgrounds, respectively. Plant spacing statistics (dpeaa)DE-He213 Crop row detection (dpeaa)DE-He213 Progressive probabilistic Hough transform (dpeaa)DE-He213 Perspective transformation (dpeaa)DE-He213 Ullah, Muhib (orcid)0000-0003-4943-2405 aut Bais, Abdul aut Enthalten in Neural computing & applications London : Springer, 1993 35(2023), 35 vom: 14. Sept., Seite 24877-24896 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2023 number:35 day:14 month:09 pages:24877-24896 https://dx.doi.org/10.1007/s00521-023-08961-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 35 14 09 24877-24896
language	English
source	Enthalten in Neural computing & applications 35(2023), 35 vom: 14. Sept., Seite 24877-24896 volume:35 year:2023 number:35 day:14 month:09 pages:24877-24896
sourceStr	Enthalten in Neural computing & applications 35(2023), 35 vom: 14. Sept., Seite 24877-24896 volume:35 year:2023 number:35 day:14 month:09 pages:24877-24896
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Plant spacing statistics Crop row detection Progressive probabilistic Hough transform Perspective transformation
isfreeaccess_bool	false
container_title	Neural computing & applications
authorswithroles_txt_mv	Islam, Fatimah @@aut@@ Ullah, Muhib @@aut@@ Bais, Abdul @@aut@@
publishDateDaySort_date	2023-09-14T00:00:00Z
hierarchy_top_id	271595574
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author	Islam, Fatimah
spellingShingle	Islam, Fatimah misc Plant spacing statistics misc Crop row detection misc Progressive probabilistic Hough transform misc Perspective transformation QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions
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topic_title	QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions Plant spacing statistics (dpeaa)DE-He213 Crop row detection (dpeaa)DE-He213 Progressive probabilistic Hough transform (dpeaa)DE-He213 Perspective transformation (dpeaa)DE-He213
topic	misc Plant spacing statistics misc Crop row detection misc Progressive probabilistic Hough transform misc Perspective transformation
topic_unstemmed	misc Plant spacing statistics misc Crop row detection misc Progressive probabilistic Hough transform misc Perspective transformation
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title	QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions
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title_full	QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions
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title_sort	quancro: a novel framework for quantification of corn crops’ consistency under natural field conditions
title_auth	QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions
abstract	Abstract Crop population and inter-plant spacing in corn farms can provide useful insight into plant phenotypic analysis and informed establishment decisions, improving crop productivity. Traditionally, farmers have relied on manual inspection to assess crop consistency, such as counting plant stands and manually estimating plant spacing. This assessment is carried out in small predefined areas, leading to insufficient crop consistency analysis on the entire field. Moreover, alternative computer vision techniques computing only one or two key parameters also prove insufficient for accurate crop consistency assessment. This research presents a framework called QuanCro that utilizes red–green–blue images from field machinery to analyze crop parameters such as plant stands counting, plant emergence rate, and plant spacing. It utilizes the state-of-the-art object detection network—You Only Look Once version 7 (YOLOv7), to locate and count corn plants combined with our proposed semantic segmentation model, Small Pyramid-UNet (SP-UNet) architecture, to determine leaf area index. This architecture is designed to be memory efficient and computationally less expensive than similar networks, such as HRNet_Mscale (72.1M) and SegNet (34.65M), as it has approximately 21M parameters. The SP-UNet is further integrated with the Zhang–Suen thinning technique and progressive probabilistic Hough transform for crop row detection and plant spacing information. QuanCro accurately estimates crop densities and identifies inconsistent crop areas. The method is tested using 8000 images and shows a mean average precision of 0.976 for identifying plant stands. The SP-UNet achieves intersection over union scores of 0.973, 0.924, and 0.926 for crops, rows, and backgrounds, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Crop population and inter-plant spacing in corn farms can provide useful insight into plant phenotypic analysis and informed establishment decisions, improving crop productivity. Traditionally, farmers have relied on manual inspection to assess crop consistency, such as counting plant stands and manually estimating plant spacing. This assessment is carried out in small predefined areas, leading to insufficient crop consistency analysis on the entire field. Moreover, alternative computer vision techniques computing only one or two key parameters also prove insufficient for accurate crop consistency assessment. This research presents a framework called QuanCro that utilizes red–green–blue images from field machinery to analyze crop parameters such as plant stands counting, plant emergence rate, and plant spacing. It utilizes the state-of-the-art object detection network—You Only Look Once version 7 (YOLOv7), to locate and count corn plants combined with our proposed semantic segmentation model, Small Pyramid-UNet (SP-UNet) architecture, to determine leaf area index. This architecture is designed to be memory efficient and computationally less expensive than similar networks, such as HRNet_Mscale (72.1M) and SegNet (34.65M), as it has approximately 21M parameters. The SP-UNet is further integrated with the Zhang–Suen thinning technique and progressive probabilistic Hough transform for crop row detection and plant spacing information. QuanCro accurately estimates crop densities and identifies inconsistent crop areas. The method is tested using 8000 images and shows a mean average precision of 0.976 for identifying plant stands. The SP-UNet achieves intersection over union scores of 0.973, 0.924, and 0.926 for crops, rows, and backgrounds, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Crop population and inter-plant spacing in corn farms can provide useful insight into plant phenotypic analysis and informed establishment decisions, improving crop productivity. Traditionally, farmers have relied on manual inspection to assess crop consistency, such as counting plant stands and manually estimating plant spacing. This assessment is carried out in small predefined areas, leading to insufficient crop consistency analysis on the entire field. Moreover, alternative computer vision techniques computing only one or two key parameters also prove insufficient for accurate crop consistency assessment. This research presents a framework called QuanCro that utilizes red–green–blue images from field machinery to analyze crop parameters such as plant stands counting, plant emergence rate, and plant spacing. It utilizes the state-of-the-art object detection network—You Only Look Once version 7 (YOLOv7), to locate and count corn plants combined with our proposed semantic segmentation model, Small Pyramid-UNet (SP-UNet) architecture, to determine leaf area index. This architecture is designed to be memory efficient and computationally less expensive than similar networks, such as HRNet_Mscale (72.1M) and SegNet (34.65M), as it has approximately 21M parameters. The SP-UNet is further integrated with the Zhang–Suen thinning technique and progressive probabilistic Hough transform for crop row detection and plant spacing information. QuanCro accurately estimates crop densities and identifies inconsistent crop areas. The method is tested using 8000 images and shows a mean average precision of 0.976 for identifying plant stands. The SP-UNet achieves intersection over union scores of 0.973, 0.924, and 0.926 for crops, rows, and backgrounds, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	35
title_short	QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions
url	https://dx.doi.org/10.1007/s00521-023-08961-8
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author2	Ullah, Muhib Bais, Abdul
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doi_str	10.1007/s00521-023-08961-8
up_date	2024-07-03T21:49:43.263Z
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QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions

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