关键词: 高光谱成像技术, 轻微损伤识别, 有效波段, 连续投影法, 二阶导数法, 机器学习

Abstract: In order to choose the effective band suitable for the development of automatic grading instruments for slight bruises on apples, the experiment took 200 Yantai Fuji apples as the research object. Firstly, hyperspectral images of intact and bruised samples after 0, 0.5, 1 h were obtained by hyperspectral imaging system across the wavelength rande of 400～1 000 nm, and reflectance of all pixels in region of interest (ROI) was extracted by ENVI 5.2 software. Then, the characteristic wavelengths, were extracted by different effective wavelengths selection methods including successive projections algorithm(SPA), xloading weights(xLW) and second derivative, and each method extracts 3, 9 and 20 characteristic wavelengths, respectively. The support vector machine (SVM) and BP neural network model based on genetic algorithm (GABP) optimization were built respectively through the feature wavebands selected by three methods. The results showed that the accuracy rate of three SVM models for the test set were built through the feature wavebands were higher than the models based on GABP. Finally, the SVM model was established respectively by using each of the characteristic wavelengths extracted by the three methods as variables. It was found that the recognition rate of the band 811 nm reached 90.625%, which was better than other bands, and was determined as the optimal band for the recognition of slight bruises on apples.

Key words: hyperspectral imaging technique, slight bruises recognition, effective wavelength, successive projections algorithm, second derivative method, machine learning