JIA-2018-09

2002 TU Ke-ling et al. Journal of Integrative Agriculture 2018, 17(9): 1999–2006 process and was used for an independent assessment of the final network. The input layer had 15 nodes which were related to our physical features of ten colors, three dimensions, weight and density. Z -score normalization was used to standardize the input features. The output layer was made of nodes related to two categories: germinated seed (1), and un-germinated seed (0). Predictivemodelbasedonbinarylogisticregression Binary logistic regression is used to predict the probability of occurrence of an event by fitting data into a logistic curve. When there is a correlation between variables and dependent variables, binary logistic regression can be used to generate a predictive model using several variables (Boz 2016). Although this approach has rarely been reported with pepper seed selection, we applied this method to generate a predictive model for predicting if the pepper seeds can germinate or not. In this research, two categories A and B were respectively used as independent variables and germinated seed (1) and un-germinated seed (0) were used as dependent variables. All data were analyzed by the statistical analysis software SPSS. Approximately 50% of the total pepper seeds, including germinated and un-germinated, were used to create the model. The rest were used to validate the model’s functionality. Seeds used for the model development and model validation sets were selected at random by Bernoulli variates with a probability parameter of 0.5. Binary logistic regression was used to explore the relationship between the features of pepper seeds and the probability of these seeds to germinate. The variables related to the probability of pepper seed to germinate are as follows: π i = 1 1+ e –( b 0 + b 1 x i 1 + b 2 x i 2 +... b j x ij ) (1) Where, π i was the probability of the i th case in the event that a pepper seed germinates; x ij was the j th variable for the i th case; and b j was the coefficient of the j th variable. In this study, a π value of “0” indicates a seed that will not germinate, whereas a π value of “1” indicates a seed that will germinate. However, π was set as the dependent variable in the model; the two categories of 15 or nine features were selected as independent variables. Z -score normalization was used to standardize the input features. The regression coefficients were estimated through an iterative maximum likelihood method. Statistics was applied to generate classification tables and evaluate the applicability of the model for estimating whether the pepper seeds germinated or not. 3. Results 3.1. Correlation analysis between pepper seed fea- tures and seed vigor As shown in Table 1, the fresh weight of seedlings was significantly positively correlated with eight physical features, R, a*, B, width, length, projected area, density and single- kernel weight ( P <0.01) and was significantly negatively correlated with hue ( P <0.01). None of the remaining features had significant correlations with seedling weight. Seed vigor was represented by the fresh weight of seedlings; therefore features that correlated with the greatest seedling fresh weight were the better predictors of vigor. The coefficients of variation indicated that the selection of pepper seeds based on the single features of a* and single-kernel weight is easier. 3.2. Pepper seed selection by the single seed feature The coefficient of correlation and the coefficients of variation indicated that pepper seed quality was best classified by a* and single-kernel weight (Table 1) where the germination percentages increased with the increase in grade-levels of a* and weight (Table 2). Germination percentage increased from 59.3 to 71.8% with the increasing grades of a* and reached a selection rate of 57.8% when the grade of a*≥3. Germination percentage increased from 59.3 to 79.4% with the increasing grades of single-kernel weight and the highest selection rate was 76.8% at a grade of ≥0.0064 g. In the verification experiment, 200 pepper seeds from the same seed lot were selected at a*≥3 and weight ≥0.0064 g. Results showed that the germination percentage of these seed were 71.3 and 72.4%, respectively, after selection. 3.3. Multilayer perceptron network classifier As shown in Table 3, of the pepper seeds in the test set categoryA, 67 seeds were predicted to germinate, however, Features x 2 x 1 H 1 H 2 H 3 H j x i ... ... 0 1 Output layer Hidden layer Input layer Fig. 2 Multilayer perceptron network topology used in this research. 1, germinated seed; 0, un-germinated seed.