Research on Refined Selection Method for Maize Seeds Based on Machine Vision Technology
In order to further improve the germination rate of maize seeds,the more suitable selection methods and parameters were discussed.Based on this,Zhengdan 958 was used as the test material in this experiment.The physical parameters of single maize seed were obtained by Seed Identification software,and the single seed germination test was carried out to study the correlation between maize seed vigor index and its morphological and physical parameters,so as to screen the optimal selection index;At the same time,the single index classification method,binary logistic regression model and multi-layer perceptron neural network model were used to predict the seed germination rate to determine the best selection method.The bud length,root length and fresh weight of seedlings were significantly correlated with the physical parameters of R,A,S and B3.According to the single index of 170≤R ≤ 190,10≤A≤20,16≤S≤24,71≤B3 ≤ 79,the germination rate increased from 66.0％to 72.1％,73.7％,75.0％and 73.6％respectively,and the selection rate was 56.8％,63.6％,52.3％and 50.8％,respectively;The seed germination rate of the binary logistic regression model method was increased to 80.9％,the seed germination selection rate was 88.4％,and the model stability rate was 97.3％;The seed germination rate of the multi-layer perceptron neural network model method was increased to 82.9％,the seed germination selection rate was 89.5％,and the model stability rate was 97.7％.In conclusion,the physical indexes R,A,S and B3 values can be used as the selection parameters of maize seeds;Compared with single index and binary logistic regression model,the multi-layer perceptron neural network model has strong advantages in predicting seed germination rate,selection rate and stability,and can be determined as the best selection method.
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