Quantitative detection of soybean rust using image processing techniques
Rust caused by Phakopsora pachyrhizi Syd. is a major constraint to soybean product in Asia. Early detection and possibilities of controlling plant diseases by the integration of several image processing methods has been the subject of extensive research. The main contribution of this paper is to pre...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Tarbiat Modares
2016-03-01
|
| Series: | Journal of Crop Protection |
| Subjects: | |
| Online Access: | http://jcp.modares.ac.ir/article-3-8488-en.html |
| _version_ | 1818551825611096064 |
|---|---|
| author | Jagadeesh Devidas Pujari Rajesh Siddarammayya Yakkundimath Shamrao Jahagirdar Abdul Byadgi |
| author_facet | Jagadeesh Devidas Pujari Rajesh Siddarammayya Yakkundimath Shamrao Jahagirdar Abdul Byadgi |
| author_sort | Jagadeesh Devidas Pujari |
| collection | DOAJ |
| description | Rust caused by Phakopsora pachyrhizi Syd. is a major constraint to soybean product in Asia. Early detection and possibilities of controlling plant diseases by the integration of several image processing methods has been the subject of extensive research. The main contribution of this paper is to present different methodologies for quantitatively detecting soybean rust at each stage of disease development, identify disease even before specific symptoms become visible and grade based on percentage of disease severity. Severity of rust infection levels at each stage of disease development was observed for 25 days on soybean leaf. Then color distribution and pixel relationship in rust infected leaf image was calculated based on global and local features for quantifying rust severity. Further, rust disease was categorized into grades based on infection severity levels and percentage disease index (PDI) was calculated. The maximum PDI of 95.5 was observed at 25th day and minimum PDI of 0.2 was observed at 6th day. |
| first_indexed | 2024-12-12T09:05:03Z |
| format | Article |
| id | doaj.art-ea12be67dc5e469390d714dcada2d0cb |
| institution | Directory Open Access Journal |
| issn | 2251-9041 2251-905X |
| language | English |
| last_indexed | 2024-12-12T09:05:03Z |
| publishDate | 2016-03-01 |
| publisher | University of Tarbiat Modares |
| record_format | Article |
| series | Journal of Crop Protection |
| spelling | doaj.art-ea12be67dc5e469390d714dcada2d0cb2022-12-22T00:29:42ZengUniversity of Tarbiat ModaresJournal of Crop Protection2251-90412251-905X2016-03-01517587Quantitative detection of soybean rust using image processing techniquesJagadeesh Devidas Pujari0Rajesh Siddarammayya Yakkundimath1Shamrao Jahagirdar2Abdul Byadgi3 S. D. M. College of Engineering and Technology, Dharwad, India. K. L. E. Institute of Technology, Hubli, India. University of Agricultural Sciences, Dharwad, India. University of Agricultural Sciences, Dharwad, India. Rust caused by Phakopsora pachyrhizi Syd. is a major constraint to soybean product in Asia. Early detection and possibilities of controlling plant diseases by the integration of several image processing methods has been the subject of extensive research. The main contribution of this paper is to present different methodologies for quantitatively detecting soybean rust at each stage of disease development, identify disease even before specific symptoms become visible and grade based on percentage of disease severity. Severity of rust infection levels at each stage of disease development was observed for 25 days on soybean leaf. Then color distribution and pixel relationship in rust infected leaf image was calculated based on global and local features for quantifying rust severity. Further, rust disease was categorized into grades based on infection severity levels and percentage disease index (PDI) was calculated. The maximum PDI of 95.5 was observed at 25th day and minimum PDI of 0.2 was observed at 6th day.http://jcp.modares.ac.ir/article-3-8488-en.htmldisease severitycolor featuresglobal regionlocal regionsoybean rust |
| spellingShingle | Jagadeesh Devidas Pujari Rajesh Siddarammayya Yakkundimath Shamrao Jahagirdar Abdul Byadgi Quantitative detection of soybean rust using image processing techniques Journal of Crop Protection disease severity color features global region local region soybean rust |
| title | Quantitative detection of soybean rust using image processing techniques |
| title_full | Quantitative detection of soybean rust using image processing techniques |
| title_fullStr | Quantitative detection of soybean rust using image processing techniques |
| title_full_unstemmed | Quantitative detection of soybean rust using image processing techniques |
| title_short | Quantitative detection of soybean rust using image processing techniques |
| title_sort | quantitative detection of soybean rust using image processing techniques |
| topic | disease severity color features global region local region soybean rust |
| url | http://jcp.modares.ac.ir/article-3-8488-en.html |
| work_keys_str_mv | AT jagadeeshdevidaspujari quantitativedetectionofsoybeanrustusingimageprocessingtechniques AT rajeshsiddarammayyayakkundimath quantitativedetectionofsoybeanrustusingimageprocessingtechniques AT shamraojahagirdar quantitativedetectionofsoybeanrustusingimageprocessingtechniques AT abdulbyadgi quantitativedetectionofsoybeanrustusingimageprocessingtechniques |