Objective:In order to realize the detection of apple sugar content, a nondestructive prediction method of apple sugar content based on the characteristic gray series of apple reflection spot image in darkroom system is proposed. Methods:The laser with the peak wavelength of 670 nm absorbed by the apple was used as the illumination light source, which was incident from the illumination port of the integrating sphere. The apple sample was placed at the sample port of the integrating sphere, and the reflection spot of the apple sample was obtained at the measuring port of the integrating sphere. Through the image collected by mobile phone, the gray information of the reflection spot image of apple under the irradiation of this wavelength was studied. It was found that the gray distribution of the reflection spot image of apple with different sugar content was different. Using partial least squares (PLS) algorithm, for 90 samples of three apple species in the training set, taking the pixel frequency (i.e. characteristic Gray Series) with gray value between 90~110 in the outer ring area of the reflected spot image as the sugar content related component, the three apple species were modeled and predicted respectively, so as to realize the nondestructive and rapid measurement of apple sugar content. Results:The predictive correlation coefficients of three kinds of apples in the training set were 0.89, 0.84 and 0.94 respectively. Based on the designed three kinds of apple sugar content prediction model, another 60 samples of the three apple species are verified. The prediction correlation coefficients of the three corresponding kinds of apple sugar degree in the verification set can reach 0.70, 0.73 and 0.76 respectively. Conclusion:Compared with the method of using multi wavelength fusion to predict apple sugar content, using a single strong absorption wavelength and the characteristic gray series of apple reflection spot image in darkroom system can be used as the basis of apple sugar content prediction, which provides a new research idea for apple sugar content prediction.

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