A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing
Abstract Image processing algorithms were employed in present study to determine the level of damage caused by mildew disease on cucumber plants. Fifty of infected plant images were randomly selected and processed with the image processing algorithm developed using image processing toolbox module of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ozguven, Mehmet Metin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) under exclusive licence to Società Italiana di Patologia Vegetale (S.I.Pa.V.) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of plant pathology - [Cham] : Springer International Publishing, 1997, 104(2022), 4 vom: 19. Juli, Seite 1397-1406 |
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| Übergeordnetes Werk: |
volume:104 ; year:2022 ; number:4 ; day:19 ; month:07 ; pages:1397-1406 |
| Links: |
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| DOI / URN: |
10.1007/s42161-022-01178-z |
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| Katalog-ID: |
SPR048841676 |
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| 100 | 1 | |a Ozguven, Mehmet Metin |e verfasserin |0 (orcid)0000-0002-6421-4804 |4 aut | |
| 245 | 1 | 2 | |a A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing |
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| 520 | |a Abstract Image processing algorithms were employed in present study to determine the level of damage caused by mildew disease on cucumber plants. Fifty of infected plant images were randomly selected and processed with the image processing algorithm developed using image processing toolbox module of MATLAB. Then the results obtained from the image processing algorithm were compared with the assessments of experts. The image processing method predicted the disease levels with 1.90 RMSE and Theil’s UII of 0.0312. Kolmogorov–Smirnov test was used to test the normality assumption of the data and test results revealed a normal distribution (p˃0.05). Determination coefficient ($ R^{2} $ = 0.995, p < 0.01) and Pearson’s correlation coefficient (r = 0.997, p < 0.01) indicated significant positive relationship between image processing and expert assessments. The study results indicated that present image processing algorithm could successfully be used in place of expert assessment for diagnosis of mildew disease in cucumber plants. | ||
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| 650 | 4 | |a Cucumber |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mildew disease |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Altas, Ziya |4 aut | |
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10.1007/s42161-022-01178-z doi (DE-627)SPR048841676 (SPR)s42161-022-01178-z-e DE-627 ger DE-627 rakwb eng Ozguven, Mehmet Metin verfasserin (orcid)0000-0002-6421-4804 aut A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Società Italiana di Patologia Vegetale (S.I.Pa.V.) 2022 Abstract Image processing algorithms were employed in present study to determine the level of damage caused by mildew disease on cucumber plants. Fifty of infected plant images were randomly selected and processed with the image processing algorithm developed using image processing toolbox module of MATLAB. Then the results obtained from the image processing algorithm were compared with the assessments of experts. The image processing method predicted the disease levels with 1.90 RMSE and Theil’s UII of 0.0312. Kolmogorov–Smirnov test was used to test the normality assumption of the data and test results revealed a normal distribution (p˃0.05). Determination coefficient ($ R^{2} $ = 0.995, p < 0.01) and Pearson’s correlation coefficient (r = 0.997, p < 0.01) indicated significant positive relationship between image processing and expert assessments. The study results indicated that present image processing algorithm could successfully be used in place of expert assessment for diagnosis of mildew disease in cucumber plants. Image processing (dpeaa)DE-He213 Cucumber (dpeaa)DE-He213 Mildew disease (dpeaa)DE-He213 Altas, Ziya aut Enthalten in Journal of plant pathology [Cham] : Springer International Publishing, 1997 104(2022), 4 vom: 19. Juli, Seite 1397-1406 (DE-627)504102400 (DE-600)2212051-8 2239-7264 nnns volume:104 year:2022 number:4 day:19 month:07 pages:1397-1406 https://dx.doi.org/10.1007/s42161-022-01178-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 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_2018 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_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 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_4346 GBV_ILN_4393 GBV_ILN_4700 AR 104 2022 4 19 07 1397-1406 |
| spelling |
10.1007/s42161-022-01178-z doi (DE-627)SPR048841676 (SPR)s42161-022-01178-z-e DE-627 ger DE-627 rakwb eng Ozguven, Mehmet Metin verfasserin (orcid)0000-0002-6421-4804 aut A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Società Italiana di Patologia Vegetale (S.I.Pa.V.) 2022 Abstract Image processing algorithms were employed in present study to determine the level of damage caused by mildew disease on cucumber plants. Fifty of infected plant images were randomly selected and processed with the image processing algorithm developed using image processing toolbox module of MATLAB. Then the results obtained from the image processing algorithm were compared with the assessments of experts. The image processing method predicted the disease levels with 1.90 RMSE and Theil’s UII of 0.0312. Kolmogorov–Smirnov test was used to test the normality assumption of the data and test results revealed a normal distribution (p˃0.05). Determination coefficient ($ R^{2} $ = 0.995, p < 0.01) and Pearson’s correlation coefficient (r = 0.997, p < 0.01) indicated significant positive relationship between image processing and expert assessments. The study results indicated that present image processing algorithm could successfully be used in place of expert assessment for diagnosis of mildew disease in cucumber plants. Image processing (dpeaa)DE-He213 Cucumber (dpeaa)DE-He213 Mildew disease (dpeaa)DE-He213 Altas, Ziya aut Enthalten in Journal of plant pathology [Cham] : Springer International Publishing, 1997 104(2022), 4 vom: 19. Juli, Seite 1397-1406 (DE-627)504102400 (DE-600)2212051-8 2239-7264 nnns volume:104 year:2022 number:4 day:19 month:07 pages:1397-1406 https://dx.doi.org/10.1007/s42161-022-01178-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 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_2018 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_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 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_4346 GBV_ILN_4393 GBV_ILN_4700 AR 104 2022 4 19 07 1397-1406 |
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10.1007/s42161-022-01178-z doi (DE-627)SPR048841676 (SPR)s42161-022-01178-z-e DE-627 ger DE-627 rakwb eng Ozguven, Mehmet Metin verfasserin (orcid)0000-0002-6421-4804 aut A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Società Italiana di Patologia Vegetale (S.I.Pa.V.) 2022 Abstract Image processing algorithms were employed in present study to determine the level of damage caused by mildew disease on cucumber plants. Fifty of infected plant images were randomly selected and processed with the image processing algorithm developed using image processing toolbox module of MATLAB. Then the results obtained from the image processing algorithm were compared with the assessments of experts. The image processing method predicted the disease levels with 1.90 RMSE and Theil’s UII of 0.0312. Kolmogorov–Smirnov test was used to test the normality assumption of the data and test results revealed a normal distribution (p˃0.05). Determination coefficient ($ R^{2} $ = 0.995, p < 0.01) and Pearson’s correlation coefficient (r = 0.997, p < 0.01) indicated significant positive relationship between image processing and expert assessments. The study results indicated that present image processing algorithm could successfully be used in place of expert assessment for diagnosis of mildew disease in cucumber plants. Image processing (dpeaa)DE-He213 Cucumber (dpeaa)DE-He213 Mildew disease (dpeaa)DE-He213 Altas, Ziya aut Enthalten in Journal of plant pathology [Cham] : Springer International Publishing, 1997 104(2022), 4 vom: 19. Juli, Seite 1397-1406 (DE-627)504102400 (DE-600)2212051-8 2239-7264 nnns volume:104 year:2022 number:4 day:19 month:07 pages:1397-1406 https://dx.doi.org/10.1007/s42161-022-01178-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 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_2018 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_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 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_4346 GBV_ILN_4393 GBV_ILN_4700 AR 104 2022 4 19 07 1397-1406 |
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10.1007/s42161-022-01178-z doi (DE-627)SPR048841676 (SPR)s42161-022-01178-z-e DE-627 ger DE-627 rakwb eng Ozguven, Mehmet Metin verfasserin (orcid)0000-0002-6421-4804 aut A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Società Italiana di Patologia Vegetale (S.I.Pa.V.) 2022 Abstract Image processing algorithms were employed in present study to determine the level of damage caused by mildew disease on cucumber plants. Fifty of infected plant images were randomly selected and processed with the image processing algorithm developed using image processing toolbox module of MATLAB. Then the results obtained from the image processing algorithm were compared with the assessments of experts. The image processing method predicted the disease levels with 1.90 RMSE and Theil’s UII of 0.0312. Kolmogorov–Smirnov test was used to test the normality assumption of the data and test results revealed a normal distribution (p˃0.05). Determination coefficient ($ R^{2} $ = 0.995, p < 0.01) and Pearson’s correlation coefficient (r = 0.997, p < 0.01) indicated significant positive relationship between image processing and expert assessments. The study results indicated that present image processing algorithm could successfully be used in place of expert assessment for diagnosis of mildew disease in cucumber plants. Image processing (dpeaa)DE-He213 Cucumber (dpeaa)DE-He213 Mildew disease (dpeaa)DE-He213 Altas, Ziya aut Enthalten in Journal of plant pathology [Cham] : Springer International Publishing, 1997 104(2022), 4 vom: 19. Juli, Seite 1397-1406 (DE-627)504102400 (DE-600)2212051-8 2239-7264 nnns volume:104 year:2022 number:4 day:19 month:07 pages:1397-1406 https://dx.doi.org/10.1007/s42161-022-01178-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 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_2018 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_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 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_4346 GBV_ILN_4393 GBV_ILN_4700 AR 104 2022 4 19 07 1397-1406 |
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10.1007/s42161-022-01178-z doi (DE-627)SPR048841676 (SPR)s42161-022-01178-z-e DE-627 ger DE-627 rakwb eng Ozguven, Mehmet Metin verfasserin (orcid)0000-0002-6421-4804 aut A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Società Italiana di Patologia Vegetale (S.I.Pa.V.) 2022 Abstract Image processing algorithms were employed in present study to determine the level of damage caused by mildew disease on cucumber plants. Fifty of infected plant images were randomly selected and processed with the image processing algorithm developed using image processing toolbox module of MATLAB. Then the results obtained from the image processing algorithm were compared with the assessments of experts. The image processing method predicted the disease levels with 1.90 RMSE and Theil’s UII of 0.0312. Kolmogorov–Smirnov test was used to test the normality assumption of the data and test results revealed a normal distribution (p˃0.05). Determination coefficient ($ R^{2} $ = 0.995, p < 0.01) and Pearson’s correlation coefficient (r = 0.997, p < 0.01) indicated significant positive relationship between image processing and expert assessments. The study results indicated that present image processing algorithm could successfully be used in place of expert assessment for diagnosis of mildew disease in cucumber plants. Image processing (dpeaa)DE-He213 Cucumber (dpeaa)DE-He213 Mildew disease (dpeaa)DE-He213 Altas, Ziya aut Enthalten in Journal of plant pathology [Cham] : Springer International Publishing, 1997 104(2022), 4 vom: 19. Juli, Seite 1397-1406 (DE-627)504102400 (DE-600)2212051-8 2239-7264 nnns volume:104 year:2022 number:4 day:19 month:07 pages:1397-1406 https://dx.doi.org/10.1007/s42161-022-01178-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 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_2018 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_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 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_4346 GBV_ILN_4393 GBV_ILN_4700 AR 104 2022 4 19 07 1397-1406 |
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Ozguven, Mehmet Metin |
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Ozguven, Mehmet Metin misc Image processing misc Cucumber misc Mildew disease A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing |
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A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing Image processing (dpeaa)DE-He213 Cucumber (dpeaa)DE-He213 Mildew disease (dpeaa)DE-He213 |
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A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing |
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new approach to detect mildew disease on cucumber (pseudoperonospora cubensis) leaves with image processing |
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A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing |
| abstract |
Abstract Image processing algorithms were employed in present study to determine the level of damage caused by mildew disease on cucumber plants. Fifty of infected plant images were randomly selected and processed with the image processing algorithm developed using image processing toolbox module of MATLAB. Then the results obtained from the image processing algorithm were compared with the assessments of experts. The image processing method predicted the disease levels with 1.90 RMSE and Theil’s UII of 0.0312. Kolmogorov–Smirnov test was used to test the normality assumption of the data and test results revealed a normal distribution (p˃0.05). Determination coefficient ($ R^{2} $ = 0.995, p < 0.01) and Pearson’s correlation coefficient (r = 0.997, p < 0.01) indicated significant positive relationship between image processing and expert assessments. The study results indicated that present image processing algorithm could successfully be used in place of expert assessment for diagnosis of mildew disease in cucumber plants. © The Author(s) under exclusive licence to Società Italiana di Patologia Vegetale (S.I.Pa.V.) 2022 |
| abstractGer |
Abstract Image processing algorithms were employed in present study to determine the level of damage caused by mildew disease on cucumber plants. Fifty of infected plant images were randomly selected and processed with the image processing algorithm developed using image processing toolbox module of MATLAB. Then the results obtained from the image processing algorithm were compared with the assessments of experts. The image processing method predicted the disease levels with 1.90 RMSE and Theil’s UII of 0.0312. Kolmogorov–Smirnov test was used to test the normality assumption of the data and test results revealed a normal distribution (p˃0.05). Determination coefficient ($ R^{2} $ = 0.995, p < 0.01) and Pearson’s correlation coefficient (r = 0.997, p < 0.01) indicated significant positive relationship between image processing and expert assessments. The study results indicated that present image processing algorithm could successfully be used in place of expert assessment for diagnosis of mildew disease in cucumber plants. © The Author(s) under exclusive licence to Società Italiana di Patologia Vegetale (S.I.Pa.V.) 2022 |
| abstract_unstemmed |
Abstract Image processing algorithms were employed in present study to determine the level of damage caused by mildew disease on cucumber plants. Fifty of infected plant images were randomly selected and processed with the image processing algorithm developed using image processing toolbox module of MATLAB. Then the results obtained from the image processing algorithm were compared with the assessments of experts. The image processing method predicted the disease levels with 1.90 RMSE and Theil’s UII of 0.0312. Kolmogorov–Smirnov test was used to test the normality assumption of the data and test results revealed a normal distribution (p˃0.05). Determination coefficient ($ R^{2} $ = 0.995, p < 0.01) and Pearson’s correlation coefficient (r = 0.997, p < 0.01) indicated significant positive relationship between image processing and expert assessments. The study results indicated that present image processing algorithm could successfully be used in place of expert assessment for diagnosis of mildew disease in cucumber plants. © The Author(s) under exclusive licence to Società Italiana di Patologia Vegetale (S.I.Pa.V.) 2022 |
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4 |
| title_short |
A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing |
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https://dx.doi.org/10.1007/s42161-022-01178-z |
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Altas, Ziya |
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| doi_str |
10.1007/s42161-022-01178-z |
| up_date |
2024-07-03T21:48:58.057Z |
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| score |
7.4021244 |