JIA-2018-09

2001 TU Ke-ling et al. Journal of Integrative Agriculture 2018, 17(9): 1999–2006 on 400 kernels of pepper seeds to determine a baseline comparison after the physical determination of seed features. The original germination percentage was 59.3%. 2.2. Statistical analysis All data, including those extracted by Seed Identification and those of weight and density, were analyzed by the statistical analysis software IBM SPSS Statistics 21 and Microsoft Excel 2013. Ten color features (R, G, B, L*, a*, b*, H, S, B, and gray), three geometric features (width, length, and projected area), and seed weight and density were employed to predict the viability of each pepper seed. Correlation analysis between each predictor (each of the 15 features) and fresh weight of seedlings was conducted. First, two predictors with the highest coefficient of variation and correlation with the fresh weight of seedlings were selected and used in single predictor models to classify and select pepper seeds. The pepper seeds were graded according to the principle of removing un-germinated seeds as much as possible for each single feature predictor. Then, classification was performed by using an artificial network, such as a multilayer perceptron. Binary logistic regression (BLR) was used to build the model in predicting the probability of seed germination to improve the selection process for high quality pepper seeds. Feature selection Color and geometrical analyses have been widely adopted in the classification process of seed quality and were applied in this research. Two categories of features were used as independent variables to select for potential predictors of seed quality and thus, high likelihood of germination: category A, all of the 15 features and category B, the nine vigor-related features (R, a*, brightness, width, length, projected area, density, single- kernel weight and hue). Multilayer perceptron network classifie One of the most common neural network topologies is the multilayer perceptron. Its efficacy has been tested in many classification tasks (Boniecki et al. 2015; Kujawa et al. 2014). Multilayer perceptron (MLP) is a feed-forward neural network, where its general architecture includes an input layer, hidden layer, and output layer. The MLP network is a function of one or more predictors that minimizes the prediction error of outputs. Fig. 2 shows the MLP topology, similar to the research of Kurtulmus et al. (2016), that was used in this study. A single hidden layer containing a sufficient number of neurons can satisfactorily predict the most complex problems, and has been confirmed in previous studies (Nazghelichi et al. 2011; Omid et al. 2009). In this study, the MLP network with one hidden layer was chosen and implemented in SPSS. Units in the hidden layers used SPSS’ hyperbolic tangent activation functions. Units in the output layer used SPSS’ identity activation functions. To avoid overfitting, we choose 50% of the data as training set, and 25% of the data as test set. A holdout set (25% of the data) was completely excluded from the training 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A B C Fig. 1 Feature extraction of pepper seeds. A, scanned image of pepper seeds. B, pre-processed image of pepper seeds. C, extracting features by Seed Identification Software developed by our lab.