MSC-CFS-ICA-based apple slight damage hyperspectral detection method

Abstract

The invention discloses an MSC-CFS-ICA-based apple slight damage hyperspectral detection method. The MSC-CFS-ICA-based apple slight damage hyperspectral detection method comprises the following stepsof acquiring an original hyperspectral image of an apple sample; extracting average spectral reflectivity data of the original hyperspectral image; preprocessing the average spectral reflectivity of an original hyperspectrum by using a multiplicative scatter correction algorithm; for the preprocessed spectral data, adopting a feature selection algorithm based on correlation, and selecting out a characteristic wavelength with high correlation with apple damage classification; and based on the characteristic wavelength, performing conversion by using independent component analysis to obtain a damage image, and obtaining a damage area through an adaptive threshold segmentation algorithm, so that the detection of apple damage is completed. The method has the beneficial effects that an AmericanSOC710VP hyperspectral imager is used for collecting hyperspectral images of Fuji apples with the waveband range of 400-1,000 nm (128 wave bands in total) in normal and different damage time.

Description

technical field [0001] The invention relates to the application technical field of applying hyperspectral technology to non-destructive detection of agricultural products, in particular to a hyperspectral detection method for slightly damaged apples based on MSC-CFS-ICA. Background technique [0002] Apple is the largest fruit in our country and one of the advantageous agricultural products in our country. Apples are easily damaged due to collision and extrusion during picking and transportation. The damage usually occurs under the peel, and the change in the appearance of the fruit is not obvious at the initial stage of damage, which is difficult to identify with the naked eye. The damaged tissue will brown within a few hours, and then cause the softening or water loss of the tissue, and finally lead to microbial infection and decay, affecting the quality. At present, most of the detection of diseases in the process of fruit collection is manual sorting and machine vision...

AI Technical Summary

Problems solved by technology

However, the characteristics of healthy and damaged areas are very similar in the visible range (400-700nm), so when the oxidative browning after fruit damage is limited, it is often difficult to detect

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