Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds
| dc.contributor.author | Baek, Insuck | |
| dc.contributor.author | Kim, Moon S. | |
| dc.contributor.author | Cho, Byoung-Kwan | |
| dc.contributor.author | Mo, Changyeun | |
| dc.contributor.author | Barnaby, Jinyoung Y. | |
| dc.contributor.author | McClung, Anna M. | |
| dc.contributor.author | Oh, Mirae | |
| dc.date.accessioned | 2019-03-29T14:40:24Z | |
| dc.date.available | 2019-03-29T14:40:24Z | |
| dc.date.issued | 2019-03-12 | |
| dc.description.abstract | The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) technique to find optimal wavelengths and develop a model for detecting discolored, diseased rice seed infected by bacterial panicle blight (Burkholderia glumae), a seedborne pathogen. For this purpose, the HSI data spanning the visible/near-infrared wavelength region between 400 and 1000 nm were collected for 500 sound and discolored rice seeds. For selecting optimal wavelengths to use for detecting diseased seed, a sequential forward selection (SFS) method combined with various spectral pretreatments was employed. To evaluate performance based on optimal wavelengths, support vector machine (SVM) and linear and quadratic discriminant analysis (LDA and QDA) models were developed for detection of discolored seeds. As a result, the violet and red regions of the visible spectrum were selected as key wavelengths reflecting the characteristics of the discolored rice seeds. When using only two or only three selected wavelengths, all of the classification methods achieved high classification accuracies over 90% for both the calibration and validation sample sets. The results of the study showed that only two to three wavelengths are needed to differentiate between discolored, diseased and sound rice, instead of using the entire HSI wavelength regions. This demonstrates the feasibility of developing a low cost multispectral imaging technology based on these selected wavelengths for non-destructive and high-throughput screening of diseased rice seed. | en_US |
| dc.description.sponsorship | This work was supported by the USDA Agricultural Research Service, Food Safety National Program [Project No. 8042-42000-020-00D]; and the National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea [Research Program for Agricultural Science & Technology Development, Project No. PJ012216]. | en_US |
| dc.description.uri | https://www.mdpi.com/2076-3417/9/5/1027 | en_US |
| dc.format.extent | 15 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.identifier | doi:10.13016/m2g5lj-bvnm | |
| dc.identifier.citation | Insuck Baek, Moon S. Kim, et.al, Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds , Appl. Sci. 2019, 9(5), 1027; https://doi.org/10.3390/app9051027 | en_US |
| dc.identifier.uri | https://doi.org/10.3390/app9051027 | |
| dc.identifier.uri | http://hdl.handle.net/11603/13268 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | MDPI | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Mechanical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | Attribution 4.0 International (CC BY 4.0) | * |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | diseased seed | en_US |
| dc.subject | hyperspectral imaging | en_US |
| dc.subject | support vector machine (SVM) | en_US |
| dc.subject | linear and quadratic discriminant analysis (LDA and QDA) | en_US |
| dc.subject | image processing | en_US |
| dc.title | Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds | en_US |
| dc.type | Text | en_US |