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 seve...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.展开更多
History of hybrid pepper seed production, the status of annually balanced production, and innovative techniques for the large-scale seed production in China are reviewed. Helped by the technological breakthroughs in t...History of hybrid pepper seed production, the status of annually balanced production, and innovative techniques for the large-scale seed production in China are reviewed. Helped by the technological breakthroughs in these fields, China has been the largest base for hybrid pepper seed production in the world.展开更多
Pepper is widely planted and used all over the world as fresh vegetable and spice.Genetic and morphological information of pepper are stored through seeds.Determination of seed variety is crucial for correctly identif...Pepper is widely planted and used all over the world as fresh vegetable and spice.Genetic and morphological information of pepper are stored through seeds.Determination of seed variety is crucial for correctly identifying genetic materials.Pepper varieties cannot be easily classified even by an expert eye due to the very small size of seeds and visual similarities.Hence,more advanced technologies are required to determine the variety of a pepper seed.A classification method was proposed to discriminate pepper seed based on neural networks and computer vision.Image acquisition was conducted using an office scanner at a resolution of 1200 dpi.Image features representing color,shape,and texture were extracted and used to classify pepper seeds.By calculating features from different color components,a feature database was constructed.Effective features were selected using sequential feature selection with different criterion functions.As a result of the feature selection procedure,the number of the features was significantly reduced from 257 to 10.Cross validation rules were applied to obtain a reliable classification model by preventing overfitting.Different numbers of neurons in the hidden layer and various training algorithms were investigated to determine the best multilayer perceptron model.The best classification performance was obtained using 30 neurons in the hidden layer of the network.With this network,an accuracy rate of 84.94%was achieved using the sequential feature selection and the training algorithm of resilient back propagation in classifying eight pepper seed varieties.展开更多
基金supported by the Beijing Municipal Science and Technology Project,China (Z151100001015004)
文摘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.
文摘History of hybrid pepper seed production, the status of annually balanced production, and innovative techniques for the large-scale seed production in China are reviewed. Helped by the technological breakthroughs in these fields, China has been the largest base for hybrid pepper seed production in the world.
文摘Pepper is widely planted and used all over the world as fresh vegetable and spice.Genetic and morphological information of pepper are stored through seeds.Determination of seed variety is crucial for correctly identifying genetic materials.Pepper varieties cannot be easily classified even by an expert eye due to the very small size of seeds and visual similarities.Hence,more advanced technologies are required to determine the variety of a pepper seed.A classification method was proposed to discriminate pepper seed based on neural networks and computer vision.Image acquisition was conducted using an office scanner at a resolution of 1200 dpi.Image features representing color,shape,and texture were extracted and used to classify pepper seeds.By calculating features from different color components,a feature database was constructed.Effective features were selected using sequential feature selection with different criterion functions.As a result of the feature selection procedure,the number of the features was significantly reduced from 257 to 10.Cross validation rules were applied to obtain a reliable classification model by preventing overfitting.Different numbers of neurons in the hidden layer and various training algorithms were investigated to determine the best multilayer perceptron model.The best classification performance was obtained using 30 neurons in the hidden layer of the network.With this network,an accuracy rate of 84.94%was achieved using the sequential feature selection and the training algorithm of resilient back propagation in classifying eight pepper seed varieties.
