Research on grade estimation of fresh corn with bracts based on image feature fusion

Grade estimation of fresh corn with bracts is mainly done manually. This work is carried out mainly by means of volume estimation by eye, hand squeezing and skinning. In order to reduce the problems of large differences in manual subjective judgment and low sorting efficiency, an image feature fusion-based estimation model for grading fresh corn with bracts was proposed in this study. The data of weight, external dimension and cob center bone line were extracted by weight sensing technology and machine vision technology respectively. Statistics such as distribution pattern, correlation and clustering were carried out on the data. Based on the support vector machine algorithm (SVM), the cob grade estimation model was constructed to discriminate the three classes. Different clusters were validated. Results demonstrate that all the eight categories of data extracted showed a normal distribution and conformed to the statistical pattern. The parameter of fresh corn with bracts is significantly correlated with the parameter of fresh corn cobs and the correlation of data of similar nature was higher than the other parameters. The correlation between length of fresh corn with bracts (LFCB) and length of fresh corn cobs (LFCC) is 0.542. The correlation between diameter of fresh corn with bracts (DFCB) and diameter of fresh corn cobs (DFCC) is 0.7. The correlation between effective length of fresh corn cob (LECC) and LFCB is 0.473. All other correlations exceeds 0.2 or more. The highest classification discrimination accuracy is achieved by using weight of fresh corn with bracts (WFCB), DFCB, and length of inflection point (LIP) as inputs, and the results of cluster analysis of LFCC, DFCC, and weight of fresh corn cob (WFCC) as outputs. The modeling accuracy is 91%. Considering weight information cluster analysis modeling is better than other clusters. The proposed method improves the efficiency of cob grade estimation for fresh corn with bracts, and provides a certain reference for the automated sorting of fresh corn.