Selection for high quality pepper seeds by machine vision and classifiers

作     者：TU Ke-ling LI Lin-juan YANG Li-ming WANG Jian-hua SUN Qun 

作者机构：Key Laboratory of Crop Genetic Improvement Department of Plant Genetics and Breeding College of Agriculture and Biotechnology China Agricultural University College of Science China Agricultural University 

出 版 物：《Journal of Integrative Agriculture》 (农业科学学报（英文版）)

年 卷 期：2018年第17卷第9期

页      面：1999-2006页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 09[农学] 0802[工学-机械工程] 0902[农学-园艺学] 090202[农学-蔬菜学] 

基　　金：supported by the Beijing Municipal Science and Technology Project China (Z151100001015004) 

主　　题：pepper seed image processing machine vision seed vigor binary logistic regression multilayer perceptron neural network 

摘      要：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 contribu