Fruit grading system based on neural network

Abstract

The invention provides an automatic fruit grading system based on fusion of multiple classifiers. The automatic fruit grading system comprises a plurality of image capturing units, a feature extracting unit, a plurality of fruit classifying units and a fusing unit, wherein the image capturing units are respectively used for capturing image information of a same fruit; the feature extracting unit is used for analyzing the image information provided by the image capturing units so as to obtain the feature information of the fruit; each of the fruit classifying units is used for classifying the fruit by adopting a respective predetermined neural network classifier according to the obtained feature information of the fruit; and the fusing unit is used for fusing the classifying results of the fruit classifying units on the basis of a predetermined fusion strategy so as to obtain a grade result of the fruit. The automatic fruit grading system has a simple and stable structure and can be applied to more industrial fields of agricultural fruit production.

Description

Technical field [0001] The invention relates to the field of fruit classification, in particular to a fruit automatic grading system based on the fusion of multiple classifiers. Background technique [0002] The fruit industry is an agricultural industry that has developed rapidly in recent years. According to statistics from the Ministry of Agriculture, my country's fruit output reached 105 million in 2007, ranking first in the world. However, due to the influence of complex factors such as man-made and natural factors in the production process of fruits, the product quality varies greatly, such as shape, size, color, etc., which are difficult to be uniform. Due to the unreasonable structure of my country's fruit varieties and backward product commercialization technology, it lacks competitiveness in the international market. [0003] Traditional artificial fruit classification is mainly based on two aspects of color and shape. Its shortcomings are low efficiency, time-consuming,...

AI Technical Summary

Problems solved by technology

[0004] However, both foreign experts and domestic researchers use a single classifier, in other words, a single classifier, which makes it difficult to effectively improve the accuracy of class discrimination

In addition, at present, most of the fruit grading process in our country adopts manual grading, and the automatic grading system is seldom used.

The shortcomings of artificial fruit grading are: it takes a long time, the result is unstable, it is easy to be disturbed by human factors, and it consumes a lot of manpower and material resources. It is not suitable for the development of fruit deep processing industry, and also restricts the competition of my country's fruit industry in the international market. force

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