Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System
Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by <i<Sphaerotheca macularis</i< (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts ma...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Md Sultan Mahmud [verfasserIn] Qamar U. Zaman [verfasserIn] Travis J. Esau [verfasserIn] Young K. Chang [verfasserIn] G. W. Price [verfasserIn] Balakrishnan Prithiviraj [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: |
In: Agronomy - MDPI AG, 2012, 10(2020), 7, p 1027 |
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| Übergeordnetes Werk: |
volume:10 ; year:2020 ; number:7, p 1027 |
| Links: |
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| DOI / URN: |
10.3390/agronomy10071027 |
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DOAJ05847420X |
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| 520 | |a Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by <i<Sphaerotheca macularis</i< (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (<8 km h<sup<−1</sup<), leaf overlapping, leaf angle, and presence of spider mite disease during field testing. | ||
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10.3390/agronomy10071027 doi (DE-627)DOAJ05847420X (DE-599)DOAJbc1e33dd28184493b9946bd113645c57 DE-627 ger DE-627 rakwb eng Md Sultan Mahmud verfasserin aut Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by <i<Sphaerotheca macularis</i< (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (<8 km h<sup<−1</sup<), leaf overlapping, leaf angle, and presence of spider mite disease during field testing. machine vision powdery mildew real-time sensing texture analysis artificial neural networks Agriculture S Qamar U. Zaman verfasserin aut Travis J. Esau verfasserin aut Young K. Chang verfasserin aut G. W. Price verfasserin aut Balakrishnan Prithiviraj verfasserin aut In Agronomy MDPI AG, 2012 10(2020), 7, p 1027 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:10 year:2020 number:7, p 1027 https://doi.org/10.3390/agronomy10071027 kostenfrei https://doaj.org/article/bc1e33dd28184493b9946bd113645c57 kostenfrei https://www.mdpi.com/2073-4395/10/7/1027 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 7, p 1027 |
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10.3390/agronomy10071027 doi (DE-627)DOAJ05847420X (DE-599)DOAJbc1e33dd28184493b9946bd113645c57 DE-627 ger DE-627 rakwb eng Md Sultan Mahmud verfasserin aut Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by <i<Sphaerotheca macularis</i< (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (<8 km h<sup<−1</sup<), leaf overlapping, leaf angle, and presence of spider mite disease during field testing. machine vision powdery mildew real-time sensing texture analysis artificial neural networks Agriculture S Qamar U. Zaman verfasserin aut Travis J. Esau verfasserin aut Young K. Chang verfasserin aut G. W. Price verfasserin aut Balakrishnan Prithiviraj verfasserin aut In Agronomy MDPI AG, 2012 10(2020), 7, p 1027 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:10 year:2020 number:7, p 1027 https://doi.org/10.3390/agronomy10071027 kostenfrei https://doaj.org/article/bc1e33dd28184493b9946bd113645c57 kostenfrei https://www.mdpi.com/2073-4395/10/7/1027 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 7, p 1027 |
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10.3390/agronomy10071027 doi (DE-627)DOAJ05847420X (DE-599)DOAJbc1e33dd28184493b9946bd113645c57 DE-627 ger DE-627 rakwb eng Md Sultan Mahmud verfasserin aut Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by <i<Sphaerotheca macularis</i< (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (<8 km h<sup<−1</sup<), leaf overlapping, leaf angle, and presence of spider mite disease during field testing. machine vision powdery mildew real-time sensing texture analysis artificial neural networks Agriculture S Qamar U. Zaman verfasserin aut Travis J. Esau verfasserin aut Young K. Chang verfasserin aut G. W. Price verfasserin aut Balakrishnan Prithiviraj verfasserin aut In Agronomy MDPI AG, 2012 10(2020), 7, p 1027 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:10 year:2020 number:7, p 1027 https://doi.org/10.3390/agronomy10071027 kostenfrei https://doaj.org/article/bc1e33dd28184493b9946bd113645c57 kostenfrei https://www.mdpi.com/2073-4395/10/7/1027 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 7, p 1027 |
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10.3390/agronomy10071027 doi (DE-627)DOAJ05847420X (DE-599)DOAJbc1e33dd28184493b9946bd113645c57 DE-627 ger DE-627 rakwb eng Md Sultan Mahmud verfasserin aut Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by <i<Sphaerotheca macularis</i< (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (<8 km h<sup<−1</sup<), leaf overlapping, leaf angle, and presence of spider mite disease during field testing. machine vision powdery mildew real-time sensing texture analysis artificial neural networks Agriculture S Qamar U. Zaman verfasserin aut Travis J. Esau verfasserin aut Young K. Chang verfasserin aut G. W. Price verfasserin aut Balakrishnan Prithiviraj verfasserin aut In Agronomy MDPI AG, 2012 10(2020), 7, p 1027 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:10 year:2020 number:7, p 1027 https://doi.org/10.3390/agronomy10071027 kostenfrei https://doaj.org/article/bc1e33dd28184493b9946bd113645c57 kostenfrei https://www.mdpi.com/2073-4395/10/7/1027 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 7, p 1027 |
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10.3390/agronomy10071027 doi (DE-627)DOAJ05847420X (DE-599)DOAJbc1e33dd28184493b9946bd113645c57 DE-627 ger DE-627 rakwb eng Md Sultan Mahmud verfasserin aut Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by <i<Sphaerotheca macularis</i< (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (<8 km h<sup<−1</sup<), leaf overlapping, leaf angle, and presence of spider mite disease during field testing. machine vision powdery mildew real-time sensing texture analysis artificial neural networks Agriculture S Qamar U. Zaman verfasserin aut Travis J. Esau verfasserin aut Young K. Chang verfasserin aut G. W. Price verfasserin aut Balakrishnan Prithiviraj verfasserin aut In Agronomy MDPI AG, 2012 10(2020), 7, p 1027 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:10 year:2020 number:7, p 1027 https://doi.org/10.3390/agronomy10071027 kostenfrei https://doaj.org/article/bc1e33dd28184493b9946bd113645c57 kostenfrei https://www.mdpi.com/2073-4395/10/7/1027 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 7, p 1027 |
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Md Sultan Mahmud |
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Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System machine vision powdery mildew real-time sensing texture analysis artificial neural networks |
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Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System |
| abstract |
Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by <i<Sphaerotheca macularis</i< (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (<8 km h<sup<−1</sup<), leaf overlapping, leaf angle, and presence of spider mite disease during field testing. |
| abstractGer |
Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by <i<Sphaerotheca macularis</i< (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (<8 km h<sup<−1</sup<), leaf overlapping, leaf angle, and presence of spider mite disease during field testing. |
| abstract_unstemmed |
Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by <i<Sphaerotheca macularis</i< (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (<8 km h<sup<−1</sup<), leaf overlapping, leaf angle, and presence of spider mite disease during field testing. |
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Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. 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