Selection for high quality pepper seeds by machine vision and classifiers
This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds. Past research has shown that seed vigor is significantly related to the seed color and size, thus several physical features were identified as candidate predictors of high seed quality....
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2018-09-01
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| Series: | Journal of Integrative Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095311918620313 |
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| author | Ke-ling TU Lin-juan LI Li-ming YANG Jian-hua WANG Qun SUN |
| author_facet | Ke-ling TU Lin-juan LI Li-ming YANG Jian-hua WANG Qun SUN |
| author_sort | Ke-ling TU |
| collection | DOAJ |
| description | This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds. Past research has shown that seed vigor is significantly related to the seed color and size, thus several physical features were identified as candidate predictors of high seed quality. Image recognition software was used to automate recognition of seed feature quality using 400 kernels of pepper cultivar 101. In addition, binary logistic regression and a neural network were applied to determine models with high predictive value of seed germination. Single-kernel germination tests were conducted to validate the predictive value of the identified features. The best predictors of seed vigor were determined by the highest correlation observed between the physical features and the subsequent fresh weight of seedlings that germinated from the 400 seeds. Correlation analysis showed that fresh weight was significantly positively correlated with eight physical features: three color features (R, a*, brightness), width, length, projected area, and single-kernel density, and weight. In contrast, fresh weight significantly negatively correlated with the feature of hue. In analyses of two of the highest correlating single features, germination percentage increased from 59.3 to 71.8% when a*≥3, and selection rate peaked at 57.8%. Germination percentage increased from 59.3 to 79.4%, and the selection rate reached 76.8%, when single-kernel weight ≥0.0064 g. The most effective model was based on a multilayer perceptron (MLP) neural network, consisting of 15 physical traits as variables, and a stability calculated as 99.4%. Germination percentage in a calibration set of seeds was 79.1% and the selection rate was 90.0%. These results indicated that the model was effective in predicting seed germination based on physical features and could be used as a guide for quality control in seed selection. Automated systems based on machine vision and model classifiers can contribute to reducing the costs and labor required in the selection of pepper seeds. |
| format | Article |
| id | doaj-art-c6a2f96a575c44d79c9c95efc2c4c790 |
| institution | Kabale University |
| issn | 2095-3119 |
| language | English |
| publishDate | 2018-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Integrative Agriculture |
| spelling | doaj-art-c6a2f96a575c44d79c9c95efc2c4c7902025-08-20T03:57:47ZengKeAi Communications Co., Ltd.Journal of Integrative Agriculture2095-31192018-09-011791999200610.1016/S2095-3119(18)62031-3Selection for high quality pepper seeds by machine vision and classifiersKe-ling TU0Lin-juan LI1Li-ming YANG2Jian-hua WANG3Qun SUN4Key Laboratory of Crop Genetic Improvement, Department of Plant Genetics and Breeding, College of Agriculture and Biotechnology, China Agricultural University, Beijing 100193, P.R.China; TU Ke-ling, Mobile: +86-18010141653Key Laboratory of Crop Genetic Improvement, Department of Plant Genetics and Breeding, College of Agriculture and Biotechnology, China Agricultural University, Beijing 100193, P.R.ChinaCollege of Science, China Agricultural University, Beijing 100083, P.R.ChinaKey Laboratory of Crop Genetic Improvement, Department of Plant Genetics and Breeding, College of Agriculture and Biotechnology, China Agricultural University, Beijing 100193, P.R.ChinaKey Laboratory of Crop Genetic Improvement, Department of Plant Genetics and Breeding, College of Agriculture and Biotechnology, China Agricultural University, Beijing 100193, P.R.China; Correspondence SUN Qun, Tel: +86-10-62732775This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds. Past research has shown that seed vigor is significantly related to the seed color and size, thus several physical features were identified as candidate predictors of high seed quality. Image recognition software was used to automate recognition of seed feature quality using 400 kernels of pepper cultivar 101. In addition, binary logistic regression and a neural network were applied to determine models with high predictive value of seed germination. Single-kernel germination tests were conducted to validate the predictive value of the identified features. The best predictors of seed vigor were determined by the highest correlation observed between the physical features and the subsequent fresh weight of seedlings that germinated from the 400 seeds. Correlation analysis showed that fresh weight was significantly positively correlated with eight physical features: three color features (R, a*, brightness), width, length, projected area, and single-kernel density, and weight. In contrast, fresh weight significantly negatively correlated with the feature of hue. In analyses of two of the highest correlating single features, germination percentage increased from 59.3 to 71.8% when a*≥3, and selection rate peaked at 57.8%. Germination percentage increased from 59.3 to 79.4%, and the selection rate reached 76.8%, when single-kernel weight ≥0.0064 g. The most effective model was based on a multilayer perceptron (MLP) neural network, consisting of 15 physical traits as variables, and a stability calculated as 99.4%. Germination percentage in a calibration set of seeds was 79.1% and the selection rate was 90.0%. These results indicated that the model was effective in predicting seed germination based on physical features and could be used as a guide for quality control in seed selection. Automated systems based on machine vision and model classifiers can contribute to reducing the costs and labor required in the selection of pepper seeds.http://www.sciencedirect.com/science/article/pii/S2095311918620313pepper seedimage processingmachine visionseed vigorbinary logistic regressionmultilayer perceptron neural network |
| spellingShingle | Ke-ling TU Lin-juan LI Li-ming YANG Jian-hua WANG Qun SUN Selection for high quality pepper seeds by machine vision and classifiers Journal of Integrative Agriculture pepper seed image processing machine vision seed vigor binary logistic regression multilayer perceptron neural network |
| title | Selection for high quality pepper seeds by machine vision and classifiers |
| title_full | Selection for high quality pepper seeds by machine vision and classifiers |
| title_fullStr | Selection for high quality pepper seeds by machine vision and classifiers |
| title_full_unstemmed | Selection for high quality pepper seeds by machine vision and classifiers |
| title_short | Selection for high quality pepper seeds by machine vision and classifiers |
| title_sort | selection for high quality pepper seeds by machine vision and classifiers |
| topic | pepper seed image processing machine vision seed vigor binary logistic regression multilayer perceptron neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2095311918620313 |
| work_keys_str_mv | AT kelingtu selectionforhighqualitypepperseedsbymachinevisionandclassifiers AT linjuanli selectionforhighqualitypepperseedsbymachinevisionandclassifiers AT limingyang selectionforhighqualitypepperseedsbymachinevisionandclassifiers AT jianhuawang selectionforhighqualitypepperseedsbymachinevisionandclassifiers AT qunsun selectionforhighqualitypepperseedsbymachinevisionandclassifiers |