Visual grading method and grading production line for pear appearance quality

Abstract

The invention discloses a visual grading method and grading production line for the pear appearance quality. According to the method, information such as the fruit shape, the color, the defect type and the defect area of a sample can be reflected through characteristic values, and an appropriate machine learning model is selected as a classifier according to the features of the characteristics for accurately prediction, finally, the information is input into a decision-making tree for a decision-making level, and a decision-making classification process of a person is simulated to obtain a classification result. A multi-model fusion method is used in the whole grading process, various kinds of information is processed in a targeted mode, the over-fitting phenomenon is avoided, the processing result is more accurate, the detection efficiency and the detection precision are improved, and the requirement for rapid and accurate detection on an assembly line is met. The method is used for rapid grading of pears on the annular production line, the pears are graded through characteristic fusion, and the identification rate can reach 95% or above. The visual grading method and grading production line are suitable for appearance quality grading of different kinds of pears.

Description

technical field [0001] The invention relates to the technical field of pear appearance quality detection and grading, in particular to a method for visual grading of pear appearance quality and a grading production line. Background technique [0002] Fruit appearance quality detection and automatic grading are of great significance to improve the market competitiveness and profit level of fresh fruits. As one of the most common fruits, pears are very necessary to detect their appearance quality. At present, the method of pear appearance quality inspection is mainly manual inspection, which consumes a lot of human resources and is highly subjective in classification, and the accuracy and efficiency are relatively low; machine vision inspection is non-contact, non-destructive, high degree of automation and It has outstanding advantages such as safety and reliability, and has been successfully applied to the appearance inspection of citrus and apples. Therefore, it is feasible ...

AI Technical Summary

Problems solved by technology

However, this device only grades according to the size characteristics of thorn pears, the classification accuracy is low, and it cannot meet the needs of pear appearance quality classification

[0005] The existing grading device can only classify one pear in the same time period, and cannot achieve efficient and continuous grading execution. The pears are pushed by the baffle, so that the pears slide directly from the platform, and collisions during the period will cause secondary damage to the pears. secondary injury, and the existing grading device does not clearly indicate the grading standard, it is impossible to determine whether the grading meets the national standard, and the grading accuracy needs to be improved

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