The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch
Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (<i>Hordeum vulgare</i> L.) grain yield and quality...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/13/7/1039 |
| _version_ | 1797212065661714432 |
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| author | Fernanda Leiva Rishap Dhakal Kristiina Himanen Rodomiro Ortiz Aakash Chawade |
| author_facet | Fernanda Leiva Rishap Dhakal Kristiina Himanen Rodomiro Ortiz Aakash Chawade |
| author_sort | Fernanda Leiva |
| collection | DOAJ |
| description | Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (<i>Hordeum vulgare</i> L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding. |
| first_indexed | 2024-04-24T10:36:28Z |
| format | Article |
| id | doaj.art-280f3d59a39747de815f99dc3e86e612 |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-04-24T10:36:28Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj.art-280f3d59a39747de815f99dc3e86e6122024-04-12T13:24:55ZengMDPI AGPlants2223-77472024-04-01137103910.3390/plants13071039The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net BlotchFernanda Leiva0Rishap Dhakal1Kristiina Himanen2Rodomiro Ortiz3Aakash Chawade4Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), P.O. Box 190, SE-23422 Lomma, SwedenDepartment of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, 1575 Linden Dr, Madison, WI 53706, USANational Plant Phenotyping Infrastructure, Helsinki Institute of Life Science, Biocenter Finland, University of Helsinki, Latokartanonkaari 7, 00790 Helsinki, FinlandDepartment of Plant Breeding, Swedish University of Agricultural Sciences (SLU), P.O. Box 190, SE-23422 Lomma, SwedenDepartment of Plant Breeding, Swedish University of Agricultural Sciences (SLU), P.O. Box 190, SE-23422 Lomma, SwedenChallenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (<i>Hordeum vulgare</i> L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding.https://www.mdpi.com/2223-7747/13/7/1039barleynet blotchdisease symptomsmachine learningRGB imaging |
| spellingShingle | Fernanda Leiva Rishap Dhakal Kristiina Himanen Rodomiro Ortiz Aakash Chawade The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch Plants barley net blotch disease symptoms machine learning RGB imaging |
| title | The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch |
| title_full | The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch |
| title_fullStr | The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch |
| title_full_unstemmed | The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch |
| title_short | The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch |
| title_sort | combination of low cost red green blue rgb image analysis and machine learning to screen for barley plant resistance to net blotch |
| topic | barley net blotch disease symptoms machine learning RGB imaging |
| url | https://www.mdpi.com/2223-7747/13/7/1039 |
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