Development of real-time onion disease monitoring system using image acquisition
In this study, real-time disease monitoring was conducted on onion which is the most representative crop in Republic of Korea, using an image acquisition system newly developed for the mobile measurement of phenotype. The purpose of this study was to improve the accuracy of prediction of disease and...
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| Format: | Article |
| Language: | English |
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Higher Education Press
2018-11-01
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| Series: | Frontiers of Agricultural Science and Engineering |
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| Online Access: | http://academic.hep.com.cn/fase/fileup/2095-7505/PDF/21847/1518407813308-473509989.pdf |
| _version_ | 1818305838608023552 |
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| author | Du-Han KIM, Kyeong-Hwan LEE, Chang-Hyun CHOI, Tae-Hyun CHOI, Yong-Joo KIM |
| author_facet | Du-Han KIM, Kyeong-Hwan LEE, Chang-Hyun CHOI, Tae-Hyun CHOI, Yong-Joo KIM |
| author_sort | Du-Han KIM, Kyeong-Hwan LEE, Chang-Hyun CHOI, Tae-Hyun CHOI, Yong-Joo KIM |
| collection | DOAJ |
| description | In this study, real-time disease monitoring was conducted on onion which is the most representative crop in Republic of Korea, using an image acquisition system newly developed for the mobile measurement of phenotype. The purpose of this study was to improve the accuracy of prediction of disease and state variables by processing images acquired from monitoring. The image acquisition system was consisted of two parts, a motorized driving system and a PTZ (pan, tilt and zoom) camera to take images of the plants. The acquired images were processed as follows. Noise was removed through an image filter and RGB (red, green and blue) colors were converted to HSV (hue, saturation and value), which enabled thresholding of areas with different colors and properties for image binarization by comparing the color of onion leaf with ambient areas. Four objects with the most significant browning in the onion leaf to the naked eye were selected as the samples for data acquired. The thresholding method with image processing was found to be superior to the naked eye in identifying accurate disease areas. In addition, it was found that the incidence of disease was different in each disease area ratio. As a result, the use of image acquisition system in image processing analysis will enable more prompt detection of any changes in the onion and monitoring of disease outbreaks during the crop lifecycle. |
| first_indexed | 2024-12-13T06:32:57Z |
| format | Article |
| id | doaj.art-60ab94e0ba3f49ffa7f5b472df0ce584 |
| institution | Directory Open Access Journal |
| issn | 2095-7505 |
| language | English |
| last_indexed | 2024-12-13T06:32:57Z |
| publishDate | 2018-11-01 |
| publisher | Higher Education Press |
| record_format | Article |
| series | Frontiers of Agricultural Science and Engineering |
| spelling | doaj.art-60ab94e0ba3f49ffa7f5b472df0ce5842022-12-21T23:56:34ZengHigher Education PressFrontiers of Agricultural Science and Engineering2095-75052018-11-015446947410.15302/J-FASE-2018213Development of real-time onion disease monitoring system using image acquisitionDu-Han KIM, Kyeong-Hwan LEE, Chang-Hyun CHOI, Tae-Hyun CHOI, Yong-Joo KIM0<sup>1</sup>. Department of Biosystems Machinery Engineering, Chung-Nam National University, Daejeon 305-764, Republic of Korea; <sup>2</sup>. Department of Rural and Biosystems Engineering, Chonnam National University, Gwangju 500-757, Republic of Korea; <sup>3</sup>. Department of Bio-Mechatronic Engineering, Sungkyunkwan University, Suwon, Gyeonggi 440-746, Republic of Korea; <sup>4</sup>. Sensoreye RD Solutions, Daejeon 302-861, Republic of KoreaIn this study, real-time disease monitoring was conducted on onion which is the most representative crop in Republic of Korea, using an image acquisition system newly developed for the mobile measurement of phenotype. The purpose of this study was to improve the accuracy of prediction of disease and state variables by processing images acquired from monitoring. The image acquisition system was consisted of two parts, a motorized driving system and a PTZ (pan, tilt and zoom) camera to take images of the plants. The acquired images were processed as follows. Noise was removed through an image filter and RGB (red, green and blue) colors were converted to HSV (hue, saturation and value), which enabled thresholding of areas with different colors and properties for image binarization by comparing the color of onion leaf with ambient areas. Four objects with the most significant browning in the onion leaf to the naked eye were selected as the samples for data acquired. The thresholding method with image processing was found to be superior to the naked eye in identifying accurate disease areas. In addition, it was found that the incidence of disease was different in each disease area ratio. As a result, the use of image acquisition system in image processing analysis will enable more prompt detection of any changes in the onion and monitoring of disease outbreaks during the crop lifecycle.http://academic.hep.com.cn/fase/fileup/2095-7505/PDF/21847/1518407813308-473509989.pdfimaging acquisition system|disease|downy mildew|onion |
| spellingShingle | Du-Han KIM, Kyeong-Hwan LEE, Chang-Hyun CHOI, Tae-Hyun CHOI, Yong-Joo KIM Development of real-time onion disease monitoring system using image acquisition Frontiers of Agricultural Science and Engineering imaging acquisition system|disease|downy mildew|onion |
| title | Development of real-time onion disease monitoring system using image acquisition |
| title_full | Development of real-time onion disease monitoring system using image acquisition |
| title_fullStr | Development of real-time onion disease monitoring system using image acquisition |
| title_full_unstemmed | Development of real-time onion disease monitoring system using image acquisition |
| title_short | Development of real-time onion disease monitoring system using image acquisition |
| title_sort | development of real time onion disease monitoring system using image acquisition |
| topic | imaging acquisition system|disease|downy mildew|onion |
| url | http://academic.hep.com.cn/fase/fileup/2095-7505/PDF/21847/1518407813308-473509989.pdf |
| work_keys_str_mv | AT duhankimkyeonghwanleechanghyunchoitaehyunchoiyongjookim developmentofrealtimeoniondiseasemonitoringsystemusingimageacquisition |