Potato Detection and Segmentation Based on Mask R-CNN
Purpose Potatoes are similar in color and size to soil and its clods. They are mostly irregular in the shape as well. Therefore, it is not easy to distinguish potatoes from the soil surface background only with machine vision. This study applied Mask R-CNN, one of the object recognition technologies...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lee, Hyeon-Seung [verfasserIn] Shin, Beom-Soo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of Biosystems Engineering - Singapore : Springer Singapore, 2004, 45(2020), 4 vom: 12. Okt., Seite 233-238 |
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| Übergeordnetes Werk: |
volume:45 ; year:2020 ; number:4 ; day:12 ; month:10 ; pages:233-238 |
| Links: |
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| DOI / URN: |
10.1007/s42853-020-00063-w |
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| Katalog-ID: |
SPR043304826 |
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| 100 | 1 | |a Lee, Hyeon-Seung |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Potato Detection and Segmentation Based on Mask R-CNN |
| 264 | 1 | |c 2020 | |
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| 520 | |a Purpose Potatoes are similar in color and size to soil and its clods. They are mostly irregular in the shape as well. Therefore, it is not easy to distinguish potatoes from the soil surface background only with machine vision. This study applied Mask R-CNN, one of the object recognition technologies using deep learning to detect potatoes. The size of object in pixel was obtained on individual potato, and they will be used to predict the yield of potatoes. Methods In order to collect the images needed for deep learning, potato images at the time of harvesting were obtained from potato farms. Annotation was entered for each irregular potato shape, where approximately 4500 potatoes were used. Resnet-101 was selected as the backbone of Mask R-CNN with a feature pyramid network. Transfer training was applied to shorten training time and limit the number of images needed to train the model. The classification performance evaluation was conducted to verify the trained model. The size of potato in pixel was obtained from the output image through the potato detection model by the segmentation algorithm using MATLAB. Results The total number of training for the potato detection model was 12,000, and the training loss of Mask R-CNN was less than 0.1%. The potato detection results from 69 randomly selected test images showed that the average detection precision was 90.8%, recall 93.0%, and F1 score 91.9%. Conclusions Potato detection model with Mask R-CNN can detect irregularly shaped potatoes on similar color soil surface. The size of the detected potato region can be extracted as well. | ||
| 650 | 4 | |a Deep learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mask R-CNN |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Potato detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Potato segmentation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Shin, Beom-Soo |e verfasserin |4 aut | |
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10.1007/s42853-020-00063-w doi (DE-627)SPR043304826 (DE-599)SPRs42853-020-00063-w-e (SPR)s42853-020-00063-w-e DE-627 ger DE-627 rakwb eng Lee, Hyeon-Seung verfasserin aut Potato Detection and Segmentation Based on Mask R-CNN 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Potatoes are similar in color and size to soil and its clods. They are mostly irregular in the shape as well. Therefore, it is not easy to distinguish potatoes from the soil surface background only with machine vision. This study applied Mask R-CNN, one of the object recognition technologies using deep learning to detect potatoes. The size of object in pixel was obtained on individual potato, and they will be used to predict the yield of potatoes. Methods In order to collect the images needed for deep learning, potato images at the time of harvesting were obtained from potato farms. Annotation was entered for each irregular potato shape, where approximately 4500 potatoes were used. Resnet-101 was selected as the backbone of Mask R-CNN with a feature pyramid network. Transfer training was applied to shorten training time and limit the number of images needed to train the model. The classification performance evaluation was conducted to verify the trained model. The size of potato in pixel was obtained from the output image through the potato detection model by the segmentation algorithm using MATLAB. Results The total number of training for the potato detection model was 12,000, and the training loss of Mask R-CNN was less than 0.1%. The potato detection results from 69 randomly selected test images showed that the average detection precision was 90.8%, recall 93.0%, and F1 score 91.9%. Conclusions Potato detection model with Mask R-CNN can detect irregularly shaped potatoes on similar color soil surface. The size of the detected potato region can be extracted as well. Deep learning (dpeaa)DE-He213 Mask R-CNN (dpeaa)DE-He213 Potato detection (dpeaa)DE-He213 Potato segmentation (dpeaa)DE-He213 Shin, Beom-Soo verfasserin aut Enthalten in Journal of Biosystems Engineering Singapore : Springer Singapore, 2004 45(2020), 4 vom: 12. Okt., Seite 233-238 (DE-627)166529633X (DE-600)2972001-1 2234-1862 nnns volume:45 year:2020 number:4 day:12 month:10 pages:233-238 https://dx.doi.org/10.1007/s42853-020-00063-w 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_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_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 45 2020 4 12 10 233-238 |
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10.1007/s42853-020-00063-w doi (DE-627)SPR043304826 (DE-599)SPRs42853-020-00063-w-e (SPR)s42853-020-00063-w-e DE-627 ger DE-627 rakwb eng Lee, Hyeon-Seung verfasserin aut Potato Detection and Segmentation Based on Mask R-CNN 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Potatoes are similar in color and size to soil and its clods. They are mostly irregular in the shape as well. Therefore, it is not easy to distinguish potatoes from the soil surface background only with machine vision. This study applied Mask R-CNN, one of the object recognition technologies using deep learning to detect potatoes. The size of object in pixel was obtained on individual potato, and they will be used to predict the yield of potatoes. Methods In order to collect the images needed for deep learning, potato images at the time of harvesting were obtained from potato farms. Annotation was entered for each irregular potato shape, where approximately 4500 potatoes were used. Resnet-101 was selected as the backbone of Mask R-CNN with a feature pyramid network. Transfer training was applied to shorten training time and limit the number of images needed to train the model. The classification performance evaluation was conducted to verify the trained model. The size of potato in pixel was obtained from the output image through the potato detection model by the segmentation algorithm using MATLAB. Results The total number of training for the potato detection model was 12,000, and the training loss of Mask R-CNN was less than 0.1%. The potato detection results from 69 randomly selected test images showed that the average detection precision was 90.8%, recall 93.0%, and F1 score 91.9%. Conclusions Potato detection model with Mask R-CNN can detect irregularly shaped potatoes on similar color soil surface. The size of the detected potato region can be extracted as well. Deep learning (dpeaa)DE-He213 Mask R-CNN (dpeaa)DE-He213 Potato detection (dpeaa)DE-He213 Potato segmentation (dpeaa)DE-He213 Shin, Beom-Soo verfasserin aut Enthalten in Journal of Biosystems Engineering Singapore : Springer Singapore, 2004 45(2020), 4 vom: 12. Okt., Seite 233-238 (DE-627)166529633X (DE-600)2972001-1 2234-1862 nnns volume:45 year:2020 number:4 day:12 month:10 pages:233-238 https://dx.doi.org/10.1007/s42853-020-00063-w 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_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_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 45 2020 4 12 10 233-238 |
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10.1007/s42853-020-00063-w doi (DE-627)SPR043304826 (DE-599)SPRs42853-020-00063-w-e (SPR)s42853-020-00063-w-e DE-627 ger DE-627 rakwb eng Lee, Hyeon-Seung verfasserin aut Potato Detection and Segmentation Based on Mask R-CNN 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Potatoes are similar in color and size to soil and its clods. They are mostly irregular in the shape as well. Therefore, it is not easy to distinguish potatoes from the soil surface background only with machine vision. This study applied Mask R-CNN, one of the object recognition technologies using deep learning to detect potatoes. The size of object in pixel was obtained on individual potato, and they will be used to predict the yield of potatoes. Methods In order to collect the images needed for deep learning, potato images at the time of harvesting were obtained from potato farms. Annotation was entered for each irregular potato shape, where approximately 4500 potatoes were used. Resnet-101 was selected as the backbone of Mask R-CNN with a feature pyramid network. Transfer training was applied to shorten training time and limit the number of images needed to train the model. The classification performance evaluation was conducted to verify the trained model. The size of potato in pixel was obtained from the output image through the potato detection model by the segmentation algorithm using MATLAB. Results The total number of training for the potato detection model was 12,000, and the training loss of Mask R-CNN was less than 0.1%. The potato detection results from 69 randomly selected test images showed that the average detection precision was 90.8%, recall 93.0%, and F1 score 91.9%. Conclusions Potato detection model with Mask R-CNN can detect irregularly shaped potatoes on similar color soil surface. The size of the detected potato region can be extracted as well. Deep learning (dpeaa)DE-He213 Mask R-CNN (dpeaa)DE-He213 Potato detection (dpeaa)DE-He213 Potato segmentation (dpeaa)DE-He213 Shin, Beom-Soo verfasserin aut Enthalten in Journal of Biosystems Engineering Singapore : Springer Singapore, 2004 45(2020), 4 vom: 12. Okt., Seite 233-238 (DE-627)166529633X (DE-600)2972001-1 2234-1862 nnns volume:45 year:2020 number:4 day:12 month:10 pages:233-238 https://dx.doi.org/10.1007/s42853-020-00063-w 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_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_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 45 2020 4 12 10 233-238 |
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10.1007/s42853-020-00063-w doi (DE-627)SPR043304826 (DE-599)SPRs42853-020-00063-w-e (SPR)s42853-020-00063-w-e DE-627 ger DE-627 rakwb eng Lee, Hyeon-Seung verfasserin aut Potato Detection and Segmentation Based on Mask R-CNN 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Potatoes are similar in color and size to soil and its clods. They are mostly irregular in the shape as well. Therefore, it is not easy to distinguish potatoes from the soil surface background only with machine vision. This study applied Mask R-CNN, one of the object recognition technologies using deep learning to detect potatoes. The size of object in pixel was obtained on individual potato, and they will be used to predict the yield of potatoes. Methods In order to collect the images needed for deep learning, potato images at the time of harvesting were obtained from potato farms. Annotation was entered for each irregular potato shape, where approximately 4500 potatoes were used. Resnet-101 was selected as the backbone of Mask R-CNN with a feature pyramid network. Transfer training was applied to shorten training time and limit the number of images needed to train the model. The classification performance evaluation was conducted to verify the trained model. The size of potato in pixel was obtained from the output image through the potato detection model by the segmentation algorithm using MATLAB. Results The total number of training for the potato detection model was 12,000, and the training loss of Mask R-CNN was less than 0.1%. The potato detection results from 69 randomly selected test images showed that the average detection precision was 90.8%, recall 93.0%, and F1 score 91.9%. Conclusions Potato detection model with Mask R-CNN can detect irregularly shaped potatoes on similar color soil surface. The size of the detected potato region can be extracted as well. Deep learning (dpeaa)DE-He213 Mask R-CNN (dpeaa)DE-He213 Potato detection (dpeaa)DE-He213 Potato segmentation (dpeaa)DE-He213 Shin, Beom-Soo verfasserin aut Enthalten in Journal of Biosystems Engineering Singapore : Springer Singapore, 2004 45(2020), 4 vom: 12. Okt., Seite 233-238 (DE-627)166529633X (DE-600)2972001-1 2234-1862 nnns volume:45 year:2020 number:4 day:12 month:10 pages:233-238 https://dx.doi.org/10.1007/s42853-020-00063-w 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_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_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 45 2020 4 12 10 233-238 |
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10.1007/s42853-020-00063-w doi (DE-627)SPR043304826 (DE-599)SPRs42853-020-00063-w-e (SPR)s42853-020-00063-w-e DE-627 ger DE-627 rakwb eng Lee, Hyeon-Seung verfasserin aut Potato Detection and Segmentation Based on Mask R-CNN 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Potatoes are similar in color and size to soil and its clods. They are mostly irregular in the shape as well. Therefore, it is not easy to distinguish potatoes from the soil surface background only with machine vision. This study applied Mask R-CNN, one of the object recognition technologies using deep learning to detect potatoes. The size of object in pixel was obtained on individual potato, and they will be used to predict the yield of potatoes. Methods In order to collect the images needed for deep learning, potato images at the time of harvesting were obtained from potato farms. Annotation was entered for each irregular potato shape, where approximately 4500 potatoes were used. Resnet-101 was selected as the backbone of Mask R-CNN with a feature pyramid network. Transfer training was applied to shorten training time and limit the number of images needed to train the model. The classification performance evaluation was conducted to verify the trained model. The size of potato in pixel was obtained from the output image through the potato detection model by the segmentation algorithm using MATLAB. Results The total number of training for the potato detection model was 12,000, and the training loss of Mask R-CNN was less than 0.1%. The potato detection results from 69 randomly selected test images showed that the average detection precision was 90.8%, recall 93.0%, and F1 score 91.9%. Conclusions Potato detection model with Mask R-CNN can detect irregularly shaped potatoes on similar color soil surface. The size of the detected potato region can be extracted as well. Deep learning (dpeaa)DE-He213 Mask R-CNN (dpeaa)DE-He213 Potato detection (dpeaa)DE-He213 Potato segmentation (dpeaa)DE-He213 Shin, Beom-Soo verfasserin aut Enthalten in Journal of Biosystems Engineering Singapore : Springer Singapore, 2004 45(2020), 4 vom: 12. Okt., Seite 233-238 (DE-627)166529633X (DE-600)2972001-1 2234-1862 nnns volume:45 year:2020 number:4 day:12 month:10 pages:233-238 https://dx.doi.org/10.1007/s42853-020-00063-w 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_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_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 45 2020 4 12 10 233-238 |
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Enthalten in Journal of Biosystems Engineering 45(2020), 4 vom: 12. Okt., Seite 233-238 volume:45 year:2020 number:4 day:12 month:10 pages:233-238 |
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Deep learning Mask R-CNN Potato detection Potato segmentation |
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Lee, Hyeon-Seung @@aut@@ Shin, Beom-Soo @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR043304826</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210224064849.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210224s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s42853-020-00063-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR043304826</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)SPRs42853-020-00063-w-e</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s42853-020-00063-w-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lee, Hyeon-Seung</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Potato Detection and Segmentation Based on Mask R-CNN</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose Potatoes are similar in color and size to soil and its clods. They are mostly irregular in the shape as well. Therefore, it is not easy to distinguish potatoes from the soil surface background only with machine vision. This study applied Mask R-CNN, one of the object recognition technologies using deep learning to detect potatoes. The size of object in pixel was obtained on individual potato, and they will be used to predict the yield of potatoes. Methods In order to collect the images needed for deep learning, potato images at the time of harvesting were obtained from potato farms. Annotation was entered for each irregular potato shape, where approximately 4500 potatoes were used. Resnet-101 was selected as the backbone of Mask R-CNN with a feature pyramid network. Transfer training was applied to shorten training time and limit the number of images needed to train the model. The classification performance evaluation was conducted to verify the trained model. The size of potato in pixel was obtained from the output image through the potato detection model by the segmentation algorithm using MATLAB. Results The total number of training for the potato detection model was 12,000, and the training loss of Mask R-CNN was less than 0.1%. The potato detection results from 69 randomly selected test images showed that the average detection precision was 90.8%, recall 93.0%, and F1 score 91.9%. Conclusions Potato detection model with Mask R-CNN can detect irregularly shaped potatoes on similar color soil surface. 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Lee, Hyeon-Seung |
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Lee, Hyeon-Seung misc Deep learning misc Mask R-CNN misc Potato detection misc Potato segmentation Potato Detection and Segmentation Based on Mask R-CNN |
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Potato Detection and Segmentation Based on Mask R-CNN Deep learning (dpeaa)DE-He213 Mask R-CNN (dpeaa)DE-He213 Potato detection (dpeaa)DE-He213 Potato segmentation (dpeaa)DE-He213 |
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Potato Detection and Segmentation Based on Mask R-CNN |
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Potato Detection and Segmentation Based on Mask R-CNN |
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Journal of Biosystems Engineering |
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potato detection and segmentation based on mask r-cnn |
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Potato Detection and Segmentation Based on Mask R-CNN |
| abstract |
Purpose Potatoes are similar in color and size to soil and its clods. They are mostly irregular in the shape as well. Therefore, it is not easy to distinguish potatoes from the soil surface background only with machine vision. This study applied Mask R-CNN, one of the object recognition technologies using deep learning to detect potatoes. The size of object in pixel was obtained on individual potato, and they will be used to predict the yield of potatoes. Methods In order to collect the images needed for deep learning, potato images at the time of harvesting were obtained from potato farms. Annotation was entered for each irregular potato shape, where approximately 4500 potatoes were used. Resnet-101 was selected as the backbone of Mask R-CNN with a feature pyramid network. Transfer training was applied to shorten training time and limit the number of images needed to train the model. The classification performance evaluation was conducted to verify the trained model. The size of potato in pixel was obtained from the output image through the potato detection model by the segmentation algorithm using MATLAB. Results The total number of training for the potato detection model was 12,000, and the training loss of Mask R-CNN was less than 0.1%. The potato detection results from 69 randomly selected test images showed that the average detection precision was 90.8%, recall 93.0%, and F1 score 91.9%. Conclusions Potato detection model with Mask R-CNN can detect irregularly shaped potatoes on similar color soil surface. The size of the detected potato region can be extracted as well. |
| abstractGer |
Purpose Potatoes are similar in color and size to soil and its clods. They are mostly irregular in the shape as well. Therefore, it is not easy to distinguish potatoes from the soil surface background only with machine vision. This study applied Mask R-CNN, one of the object recognition technologies using deep learning to detect potatoes. The size of object in pixel was obtained on individual potato, and they will be used to predict the yield of potatoes. Methods In order to collect the images needed for deep learning, potato images at the time of harvesting were obtained from potato farms. Annotation was entered for each irregular potato shape, where approximately 4500 potatoes were used. Resnet-101 was selected as the backbone of Mask R-CNN with a feature pyramid network. Transfer training was applied to shorten training time and limit the number of images needed to train the model. The classification performance evaluation was conducted to verify the trained model. The size of potato in pixel was obtained from the output image through the potato detection model by the segmentation algorithm using MATLAB. Results The total number of training for the potato detection model was 12,000, and the training loss of Mask R-CNN was less than 0.1%. The potato detection results from 69 randomly selected test images showed that the average detection precision was 90.8%, recall 93.0%, and F1 score 91.9%. Conclusions Potato detection model with Mask R-CNN can detect irregularly shaped potatoes on similar color soil surface. The size of the detected potato region can be extracted as well. |
| abstract_unstemmed |
Purpose Potatoes are similar in color and size to soil and its clods. They are mostly irregular in the shape as well. Therefore, it is not easy to distinguish potatoes from the soil surface background only with machine vision. This study applied Mask R-CNN, one of the object recognition technologies using deep learning to detect potatoes. The size of object in pixel was obtained on individual potato, and they will be used to predict the yield of potatoes. Methods In order to collect the images needed for deep learning, potato images at the time of harvesting were obtained from potato farms. Annotation was entered for each irregular potato shape, where approximately 4500 potatoes were used. Resnet-101 was selected as the backbone of Mask R-CNN with a feature pyramid network. Transfer training was applied to shorten training time and limit the number of images needed to train the model. The classification performance evaluation was conducted to verify the trained model. The size of potato in pixel was obtained from the output image through the potato detection model by the segmentation algorithm using MATLAB. Results The total number of training for the potato detection model was 12,000, and the training loss of Mask R-CNN was less than 0.1%. The potato detection results from 69 randomly selected test images showed that the average detection precision was 90.8%, recall 93.0%, and F1 score 91.9%. Conclusions Potato detection model with Mask R-CNN can detect irregularly shaped potatoes on similar color soil surface. The size of the detected potato region can be extracted as well. |
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4 |
| title_short |
Potato Detection and Segmentation Based on Mask R-CNN |
| url |
https://dx.doi.org/10.1007/s42853-020-00063-w |
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true |
| author2 |
Shin, Beom-Soo |
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Shin, Beom-Soo |
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166529633X |
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c |
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false |
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false |
| doi_str |
10.1007/s42853-020-00063-w |
| up_date |
2024-07-03T17:49:31.291Z |
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1803581098610917376 |
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