Acre-Scale Grape Bunch Detection and Predict Grape Harvest Using YOLO Deep Learning Network
Abstract To provide the harvesting weight of grapes in real time to the farmer in the form of acres or square feet of land, the proposed system provides an estimate of grape harvest in the form of kilograms. Some of the challenges in the field where crop harvest may vary due to differences in soil,...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sneha, N. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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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| Übergeordnetes Werk: |
Enthalten in: SN Computer Science - Singapore : Springer Singapore, 2020, 5(2024), 2 vom: 03. Feb. |
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| Übergeordnetes Werk: |
volume:5 ; year:2024 ; number:2 ; day:03 ; month:02 |
| Links: |
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| DOI / URN: |
10.1007/s42979-023-02572-9 |
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| Katalog-ID: |
SPR054643538 |
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| 520 | |a Abstract To provide the harvesting weight of grapes in real time to the farmer in the form of acres or square feet of land, the proposed system provides an estimate of grape harvest in the form of kilograms. Some of the challenges in the field where crop harvest may vary due to differences in soil, the size of grapes, maturate of the grapes, color, disease, etc. The proposed system uses YoloV5, a real-time object detection system, to detect the grape bunches, and the total bunch count is based on several images collected from the forms. The total area of acres is divided into bins, which are in turn divided into sectors. The grape data are collected from the Dodballapur district; Karnataka, which consist of 4000 grape images and manual annotation was performed on each image to classify the grape bunch and leaves. Pre-trained YoloV5 is used to detect the grapes by extracting visual features from the training images. The visual images are fetched and used to build a detection network using YoloV5. The research presents three-object detection-pre-trained YOLO models such as YoloV3, YoloV4, and YoloV5, which perform detection of grape bunches. Three models are trained on grape bunches and were compared with precision, F1 Score, and recognition recall value, achieving good results with YoloV5 along with counting grapes, which helps in knowing the harvesting status of the bunch 98%. The recall and F1 Scores are 90.2, 91.25, 95.63 and 92.21, 92.68, 93.21 for YoloV3, YoloV4, YoloV5, respectively. The network also calculates the total bunches in each image, and multiplied with average gram of a bunch and provides the final kilograms expected from each acre for the set of images collected for the particular acre. | ||
| 650 | 4 | |a Agriculture |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Sundaram, Meenakshi |4 aut | |
| 700 | 1 | |a Ranjan, Rajeev |4 aut | |
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10.1007/s42979-023-02572-9 doi (DE-627)SPR054643538 (SPR)s42979-023-02572-9-e DE-627 ger DE-627 rakwb eng Sneha, N. verfasserin aut Acre-Scale Grape Bunch Detection and Predict Grape Harvest Using YOLO Deep Learning Network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 To provide the harvesting weight of grapes in real time to the farmer in the form of acres or square feet of land, the proposed system provides an estimate of grape harvest in the form of kilograms. Some of the challenges in the field where crop harvest may vary due to differences in soil, the size of grapes, maturate of the grapes, color, disease, etc. The proposed system uses YoloV5, a real-time object detection system, to detect the grape bunches, and the total bunch count is based on several images collected from the forms. The total area of acres is divided into bins, which are in turn divided into sectors. The grape data are collected from the Dodballapur district; Karnataka, which consist of 4000 grape images and manual annotation was performed on each image to classify the grape bunch and leaves. Pre-trained YoloV5 is used to detect the grapes by extracting visual features from the training images. The visual images are fetched and used to build a detection network using YoloV5. The research presents three-object detection-pre-trained YOLO models such as YoloV3, YoloV4, and YoloV5, which perform detection of grape bunches. Three models are trained on grape bunches and were compared with precision, F1 Score, and recognition recall value, achieving good results with YoloV5 along with counting grapes, which helps in knowing the harvesting status of the bunch 98%. The recall and F1 Scores are 90.2, 91.25, 95.63 and 92.21, 92.68, 93.21 for YoloV3, YoloV4, YoloV5, respectively. The network also calculates the total bunches in each image, and multiplied with average gram of a bunch and provides the final kilograms expected from each acre for the set of images collected for the particular acre. Agriculture (dpeaa)DE-He213 Grapes harvest prediction (dpeaa)DE-He213 YoloV3 (dpeaa)DE-He213 YoloV4 (dpeaa)DE-He213 YoloV5 (dpeaa)DE-He213 Sundaram, Meenakshi aut Ranjan, Rajeev aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 2 vom: 03. Feb. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:2 day:03 month:02 https://dx.doi.org/10.1007/s42979-023-02572-9 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 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_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 5 2024 2 03 02 |
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10.1007/s42979-023-02572-9 doi (DE-627)SPR054643538 (SPR)s42979-023-02572-9-e DE-627 ger DE-627 rakwb eng Sneha, N. verfasserin aut Acre-Scale Grape Bunch Detection and Predict Grape Harvest Using YOLO Deep Learning Network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 To provide the harvesting weight of grapes in real time to the farmer in the form of acres or square feet of land, the proposed system provides an estimate of grape harvest in the form of kilograms. Some of the challenges in the field where crop harvest may vary due to differences in soil, the size of grapes, maturate of the grapes, color, disease, etc. The proposed system uses YoloV5, a real-time object detection system, to detect the grape bunches, and the total bunch count is based on several images collected from the forms. The total area of acres is divided into bins, which are in turn divided into sectors. The grape data are collected from the Dodballapur district; Karnataka, which consist of 4000 grape images and manual annotation was performed on each image to classify the grape bunch and leaves. Pre-trained YoloV5 is used to detect the grapes by extracting visual features from the training images. The visual images are fetched and used to build a detection network using YoloV5. The research presents three-object detection-pre-trained YOLO models such as YoloV3, YoloV4, and YoloV5, which perform detection of grape bunches. Three models are trained on grape bunches and were compared with precision, F1 Score, and recognition recall value, achieving good results with YoloV5 along with counting grapes, which helps in knowing the harvesting status of the bunch 98%. The recall and F1 Scores are 90.2, 91.25, 95.63 and 92.21, 92.68, 93.21 for YoloV3, YoloV4, YoloV5, respectively. The network also calculates the total bunches in each image, and multiplied with average gram of a bunch and provides the final kilograms expected from each acre for the set of images collected for the particular acre. Agriculture (dpeaa)DE-He213 Grapes harvest prediction (dpeaa)DE-He213 YoloV3 (dpeaa)DE-He213 YoloV4 (dpeaa)DE-He213 YoloV5 (dpeaa)DE-He213 Sundaram, Meenakshi aut Ranjan, Rajeev aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 2 vom: 03. Feb. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:2 day:03 month:02 https://dx.doi.org/10.1007/s42979-023-02572-9 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 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_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 5 2024 2 03 02 |
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10.1007/s42979-023-02572-9 doi (DE-627)SPR054643538 (SPR)s42979-023-02572-9-e DE-627 ger DE-627 rakwb eng Sneha, N. verfasserin aut Acre-Scale Grape Bunch Detection and Predict Grape Harvest Using YOLO Deep Learning Network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 To provide the harvesting weight of grapes in real time to the farmer in the form of acres or square feet of land, the proposed system provides an estimate of grape harvest in the form of kilograms. Some of the challenges in the field where crop harvest may vary due to differences in soil, the size of grapes, maturate of the grapes, color, disease, etc. The proposed system uses YoloV5, a real-time object detection system, to detect the grape bunches, and the total bunch count is based on several images collected from the forms. The total area of acres is divided into bins, which are in turn divided into sectors. The grape data are collected from the Dodballapur district; Karnataka, which consist of 4000 grape images and manual annotation was performed on each image to classify the grape bunch and leaves. Pre-trained YoloV5 is used to detect the grapes by extracting visual features from the training images. The visual images are fetched and used to build a detection network using YoloV5. The research presents three-object detection-pre-trained YOLO models such as YoloV3, YoloV4, and YoloV5, which perform detection of grape bunches. Three models are trained on grape bunches and were compared with precision, F1 Score, and recognition recall value, achieving good results with YoloV5 along with counting grapes, which helps in knowing the harvesting status of the bunch 98%. The recall and F1 Scores are 90.2, 91.25, 95.63 and 92.21, 92.68, 93.21 for YoloV3, YoloV4, YoloV5, respectively. The network also calculates the total bunches in each image, and multiplied with average gram of a bunch and provides the final kilograms expected from each acre for the set of images collected for the particular acre. Agriculture (dpeaa)DE-He213 Grapes harvest prediction (dpeaa)DE-He213 YoloV3 (dpeaa)DE-He213 YoloV4 (dpeaa)DE-He213 YoloV5 (dpeaa)DE-He213 Sundaram, Meenakshi aut Ranjan, Rajeev aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 2 vom: 03. Feb. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:2 day:03 month:02 https://dx.doi.org/10.1007/s42979-023-02572-9 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 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_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 5 2024 2 03 02 |
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10.1007/s42979-023-02572-9 doi (DE-627)SPR054643538 (SPR)s42979-023-02572-9-e DE-627 ger DE-627 rakwb eng Sneha, N. verfasserin aut Acre-Scale Grape Bunch Detection and Predict Grape Harvest Using YOLO Deep Learning Network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 To provide the harvesting weight of grapes in real time to the farmer in the form of acres or square feet of land, the proposed system provides an estimate of grape harvest in the form of kilograms. Some of the challenges in the field where crop harvest may vary due to differences in soil, the size of grapes, maturate of the grapes, color, disease, etc. The proposed system uses YoloV5, a real-time object detection system, to detect the grape bunches, and the total bunch count is based on several images collected from the forms. The total area of acres is divided into bins, which are in turn divided into sectors. The grape data are collected from the Dodballapur district; Karnataka, which consist of 4000 grape images and manual annotation was performed on each image to classify the grape bunch and leaves. Pre-trained YoloV5 is used to detect the grapes by extracting visual features from the training images. The visual images are fetched and used to build a detection network using YoloV5. The research presents three-object detection-pre-trained YOLO models such as YoloV3, YoloV4, and YoloV5, which perform detection of grape bunches. Three models are trained on grape bunches and were compared with precision, F1 Score, and recognition recall value, achieving good results with YoloV5 along with counting grapes, which helps in knowing the harvesting status of the bunch 98%. The recall and F1 Scores are 90.2, 91.25, 95.63 and 92.21, 92.68, 93.21 for YoloV3, YoloV4, YoloV5, respectively. The network also calculates the total bunches in each image, and multiplied with average gram of a bunch and provides the final kilograms expected from each acre for the set of images collected for the particular acre. Agriculture (dpeaa)DE-He213 Grapes harvest prediction (dpeaa)DE-He213 YoloV3 (dpeaa)DE-He213 YoloV4 (dpeaa)DE-He213 YoloV5 (dpeaa)DE-He213 Sundaram, Meenakshi aut Ranjan, Rajeev aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 2 vom: 03. Feb. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:2 day:03 month:02 https://dx.doi.org/10.1007/s42979-023-02572-9 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 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_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 5 2024 2 03 02 |
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10.1007/s42979-023-02572-9 doi (DE-627)SPR054643538 (SPR)s42979-023-02572-9-e DE-627 ger DE-627 rakwb eng Sneha, N. verfasserin aut Acre-Scale Grape Bunch Detection and Predict Grape Harvest Using YOLO Deep Learning Network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 To provide the harvesting weight of grapes in real time to the farmer in the form of acres or square feet of land, the proposed system provides an estimate of grape harvest in the form of kilograms. Some of the challenges in the field where crop harvest may vary due to differences in soil, the size of grapes, maturate of the grapes, color, disease, etc. The proposed system uses YoloV5, a real-time object detection system, to detect the grape bunches, and the total bunch count is based on several images collected from the forms. The total area of acres is divided into bins, which are in turn divided into sectors. The grape data are collected from the Dodballapur district; Karnataka, which consist of 4000 grape images and manual annotation was performed on each image to classify the grape bunch and leaves. Pre-trained YoloV5 is used to detect the grapes by extracting visual features from the training images. The visual images are fetched and used to build a detection network using YoloV5. The research presents three-object detection-pre-trained YOLO models such as YoloV3, YoloV4, and YoloV5, which perform detection of grape bunches. Three models are trained on grape bunches and were compared with precision, F1 Score, and recognition recall value, achieving good results with YoloV5 along with counting grapes, which helps in knowing the harvesting status of the bunch 98%. The recall and F1 Scores are 90.2, 91.25, 95.63 and 92.21, 92.68, 93.21 for YoloV3, YoloV4, YoloV5, respectively. The network also calculates the total bunches in each image, and multiplied with average gram of a bunch and provides the final kilograms expected from each acre for the set of images collected for the particular acre. Agriculture (dpeaa)DE-He213 Grapes harvest prediction (dpeaa)DE-He213 YoloV3 (dpeaa)DE-He213 YoloV4 (dpeaa)DE-He213 YoloV5 (dpeaa)DE-He213 Sundaram, Meenakshi aut Ranjan, Rajeev aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2024), 2 vom: 03. Feb. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:2 day:03 month:02 https://dx.doi.org/10.1007/s42979-023-02572-9 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 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_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 5 2024 2 03 02 |
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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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract To provide the harvesting weight of grapes in real time to the farmer in the form of acres or square feet of land, the proposed system provides an estimate of grape harvest in the form of kilograms. Some of the challenges in the field where crop harvest may vary due to differences in soil, the size of grapes, maturate of the grapes, color, disease, etc. The proposed system uses YoloV5, a real-time object detection system, to detect the grape bunches, and the total bunch count is based on several images collected from the forms. 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Sneha, N. |
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Sneha, N. misc Agriculture misc Grapes harvest prediction misc YoloV3 misc YoloV4 misc YoloV5 Acre-Scale Grape Bunch Detection and Predict Grape Harvest Using YOLO Deep Learning Network |
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Acre-Scale Grape Bunch Detection and Predict Grape Harvest Using YOLO Deep Learning Network Agriculture (dpeaa)DE-He213 Grapes harvest prediction (dpeaa)DE-He213 YoloV3 (dpeaa)DE-He213 YoloV4 (dpeaa)DE-He213 YoloV5 (dpeaa)DE-He213 |
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acre-scale grape bunch detection and predict grape harvest using yolo deep learning network |
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Acre-Scale Grape Bunch Detection and Predict Grape Harvest Using YOLO Deep Learning Network |
| abstract |
Abstract To provide the harvesting weight of grapes in real time to the farmer in the form of acres or square feet of land, the proposed system provides an estimate of grape harvest in the form of kilograms. Some of the challenges in the field where crop harvest may vary due to differences in soil, the size of grapes, maturate of the grapes, color, disease, etc. The proposed system uses YoloV5, a real-time object detection system, to detect the grape bunches, and the total bunch count is based on several images collected from the forms. The total area of acres is divided into bins, which are in turn divided into sectors. The grape data are collected from the Dodballapur district; Karnataka, which consist of 4000 grape images and manual annotation was performed on each image to classify the grape bunch and leaves. Pre-trained YoloV5 is used to detect the grapes by extracting visual features from the training images. The visual images are fetched and used to build a detection network using YoloV5. The research presents three-object detection-pre-trained YOLO models such as YoloV3, YoloV4, and YoloV5, which perform detection of grape bunches. Three models are trained on grape bunches and were compared with precision, F1 Score, and recognition recall value, achieving good results with YoloV5 along with counting grapes, which helps in knowing the harvesting status of the bunch 98%. The recall and F1 Scores are 90.2, 91.25, 95.63 and 92.21, 92.68, 93.21 for YoloV3, YoloV4, YoloV5, respectively. The network also calculates the total bunches in each image, and multiplied with average gram of a bunch and provides the final kilograms expected from each acre for the set of images collected for the particular acre. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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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Abstract To provide the harvesting weight of grapes in real time to the farmer in the form of acres or square feet of land, the proposed system provides an estimate of grape harvest in the form of kilograms. Some of the challenges in the field where crop harvest may vary due to differences in soil, the size of grapes, maturate of the grapes, color, disease, etc. The proposed system uses YoloV5, a real-time object detection system, to detect the grape bunches, and the total bunch count is based on several images collected from the forms. The total area of acres is divided into bins, which are in turn divided into sectors. The grape data are collected from the Dodballapur district; Karnataka, which consist of 4000 grape images and manual annotation was performed on each image to classify the grape bunch and leaves. Pre-trained YoloV5 is used to detect the grapes by extracting visual features from the training images. The visual images are fetched and used to build a detection network using YoloV5. The research presents three-object detection-pre-trained YOLO models such as YoloV3, YoloV4, and YoloV5, which perform detection of grape bunches. Three models are trained on grape bunches and were compared with precision, F1 Score, and recognition recall value, achieving good results with YoloV5 along with counting grapes, which helps in knowing the harvesting status of the bunch 98%. The recall and F1 Scores are 90.2, 91.25, 95.63 and 92.21, 92.68, 93.21 for YoloV3, YoloV4, YoloV5, respectively. The network also calculates the total bunches in each image, and multiplied with average gram of a bunch and provides the final kilograms expected from each acre for the set of images collected for the particular acre. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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 To provide the harvesting weight of grapes in real time to the farmer in the form of acres or square feet of land, the proposed system provides an estimate of grape harvest in the form of kilograms. Some of the challenges in the field where crop harvest may vary due to differences in soil, the size of grapes, maturate of the grapes, color, disease, etc. The proposed system uses YoloV5, a real-time object detection system, to detect the grape bunches, and the total bunch count is based on several images collected from the forms. The total area of acres is divided into bins, which are in turn divided into sectors. The grape data are collected from the Dodballapur district; Karnataka, which consist of 4000 grape images and manual annotation was performed on each image to classify the grape bunch and leaves. Pre-trained YoloV5 is used to detect the grapes by extracting visual features from the training images. The visual images are fetched and used to build a detection network using YoloV5. The research presents three-object detection-pre-trained YOLO models such as YoloV3, YoloV4, and YoloV5, which perform detection of grape bunches. Three models are trained on grape bunches and were compared with precision, F1 Score, and recognition recall value, achieving good results with YoloV5 along with counting grapes, which helps in knowing the harvesting status of the bunch 98%. The recall and F1 Scores are 90.2, 91.25, 95.63 and 92.21, 92.68, 93.21 for YoloV3, YoloV4, YoloV5, respectively. The network also calculates the total bunches in each image, and multiplied with average gram of a bunch and provides the final kilograms expected from each acre for the set of images collected for the particular acre. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. 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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Acre-Scale Grape Bunch Detection and Predict Grape Harvest Using YOLO Deep Learning Network |
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Sundaram, Meenakshi Ranjan, Rajeev |
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10.1007/s42979-023-02572-9 |
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2024-07-04T02:29:26.856Z |
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| score |
7.3989687 |