| In the production process of sweet corn,there are many problems such as low seed vigor and low field seedling emergence.In this study,four varieties of sweet corn seeds were used as materials.Based on exploring the differences in seed vigor of different varieties of sweet corn,machine vision technology was used for identification and extraction.Seed physical parameters combined with artificial neural network and binary logistic regression analysis methods establish the seed viability prediction and screening model.At the same time,the liquid gravity method is used to select different types of sweet corn seeds and determine the appropriate drying method.The research results provide theoretical basis for the automatic detection and fine selection of sweet corn seed viability,and it has positive significance in promoting machine vision technology and data modeling in the detection of sweet corn seed viability.The results of the study are as follows:(1)The difference in seed vigor between 4 sweet corn varieties is obvious,and they were divided into high vigor varieties 601 and 1618,which germination percentages were 92.0%and 93.3%,two low vigor varieties 867 and Jinfei which germination percentages were 74%and 71.3% respectively.867 and Jinfei’s grain storage material conversion rate and grain oil content lower than the two high vigor varieties,starch content is higher.Therefore,the reasons for the low vigor of sweet corn seeds are the low conversion rate of storage materials,low oil content,and high starch content in grains.The results of adversity germination tests showed that the high-vigor varieties 601 and 1618 could still maintain high vigor in adversity,among which 601 had the best anti-aging ability among the 4 varieties,and the variety 1618 had the best salt tolerance,drought tolerance and cold-resistance ability.The low vigor varieties 867 and Jinfei have weaker resistance to adversity,of which Jinfei is the least able to resist the high-salt environment,and the variety 867 has the worst ability to resist drought.(2)The a,b,Hues,Saturation,width,and projected area of Jinfei sweet corn seeds extracted based on machine vision technology were significantly higher than those of seeds(average seedling length,average root length,and average fresh weight).Or significantly related;among them b ≤ 20,the germination percentage can be increased from 71.3% to75.8%,the winning rate is 86.2%,the average seedling length is increased by 0.3cm,theaverage root length is increased by 0.6cm;Hues of variety 867 sweet corn seeds was significantly correlated with the vitality index.According to Hues ≤ 38,the germination percentage of the single index selection can be increased from 76.0% to 81.8%,the selection rate can reach 73.7%,the average seedling length is increased by 0.36 cm,and the average root length is increased by 0.81 cm.(3)Based on the binary logistic regression to establish a vitality detection model,the overall prediction accuracy of the model established by the 13 physical indicators of the Jinfei sweet seeds was 70.3%,the live seed recognition rate was 90.2%,and the germination percentage could be increased to 74.2%;The overall prediction rate of the binary physical logistic regression model for the 13 physical indicators of 867 seeds was 76.9%,of which the live seed recognition rate was 90.0% and the germination percentage was 81.8%;modeling with 4 principal components was not effective.Based on the artificial neural network,a double hidden layer model was constructed with 13 physical indicators of the Jinfei sweet corn seeds.The overall prediction accuracy rate of the model was 74.2%,of which the good seed recognition rate was 93.8%.After the model prediction,the germination percentage could be increased to 76.9%.The single hidden layer model with 13 physical indexes of variety 867 has the best effect.The good seed recognition rate is 98.5% and the overall model accuracy is 77.5%.After the model prediction,the germination percentage can reach 79.2%.The effect of artificial neural network modeling is better than the binary logistic regression model.Therefore,the neural network with double hidden layer model builted with 13 physical indicators of the Jinfei sweet corn seeds,93.8% of viable Jinfei sweet corn seeds can be identified,which is an effective method for rapid detection of the seed viability of the Jinfei sweet corn.(4)Single seed germination test results showed that the varieties of Jinfei proportion and single grain weight and seed vigor index had a significant negative correlation,On this basis,the liquid sorting technology of sweet corn seed was studied from the aspects of the preparation of the suitable specific gravity liquid and the suitable drying of the seed after liquid separation.The seed germination potential,germination percentage and average fresh weight of seedlings in the range of 1.10<ρ≤1.25 were significantly higher than those of CK and the other two seeds.The germination power and germination percentage increased by10.0% and 3.4%,respectively.Drying the Jinfei and the 867 sweet corn seeds at 43°C using aHG-10 dryer had a lower impact on seed vigor.The seed germination percentage of Jinfei seeds after drying at 43°C-0 r/min was 72.7%.The germination potential,germination percentage and simple vigor index had no significant difference with CK.The 867 sweet corn seeds were dried at 43°C-5 by HG-10 dryer.After r/min drying,the germination power and germination percentage increased compared with CK,but there was no significant difference.Compared with other drying methods,using the HG-10 dryer to dry the seed,because the internal drying drum rotates at a uniform speed,the seed pile heats more evenly,so the drying has less damage to the seed and has certain advantages. |
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