Rapid nondestructive apple wormhole testing method based on hyperspectral imaging technology

Abstract

The invention provides a rapid nondestructive apple wormhole testing method based on a hyperspectral imaging technology. The rapid nondestructive testing method comprises the following steps: collecting information of an apple spectrum by using a hyperspectral imaging system; analyzing the characteristics of an apple spectrum curve; carrying out main component analysis on spectrum data; meanwhile, determining a single-waveband characteristic image according to a characteristic wavelength; separating to extract apples and wormholes in sequence by using a maximum entropy classification threshold value separation method; extracting an interested area of a PC1 (Principal Component 1) image in sequence; extracting textural features and spectrum characteristics of the interested area; and finally, rapidly and nondestructively detecting apple wormholes by using a BP (Back Propagation) neural network. The method has the advantages of high detection speed, and simplicity and convenience in operation and the precision of a detection result is high; furthermore, the apples can be prevented from being damaged.

Description

Technical field [0001] The present invention is a method of non -destructive detection, especially a method of fast, non -destructive detection of appleworm eyes, which is specifically to achieve the purpose of fast non -destructive detection through data processing of high spectral imaging technology. Background technique Apple is one of the largest breeds in my country.Because of its rich vitamin nutrition, it is the crown of the world's four major fruits (apples, grapes, citrus and bananas).However, in the growth process of Apple, it is often affected by various factors and erosion of various insect pests, which leads to Apple's eye -catching eye, which has lost its edibleness and greatly affects the quality and sales of Apple.It can be seen that the eye detection of fresh apples is particularly important.Most of the traditional testing methods are manual operations, time -consuming and labor -intensive, and low efficiency, and cannot meet the needs of large -scale production...

AI Technical Summary

Problems solved by technology

Traditional detection methods are mostly manual operations, time-consuming, labor-intensive, and inefficient, and cannot meet the needs of mass production

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