Recognition method of insect damage and stem/calyx on apple based on hyperspectral imaging

Abstract: Insect damage is one of the main defects of the apple, which could make it lose edibility and greatly reduce the quality of the apple and commercial value. So whether apples exist insect damage is one of the important indicators of the grade apple quality. In this study, we aim to nondestructively detect insect damage on apples under the interference of stem / calyx with hyperspectral imaging technique. 160 'Red Fuji' apples，including 80 intact and 80 insect infected, picked from an apple planting demonstration garden in the Shenbei New Area in Shenyang city. A hyperspectral imaging collection system with the wavelength range of 400-1 000 nm and spectral resolution ratio of 2.8 mm was established in order to acquire the hyperspectral images of these apple samples. These acquisition apples hyperspectral images were carried out black and white plate correction in order to eliminate the noise generated hyperspectral imaging instrument in the acquisition process. The extraction and analysis of spectral reflectance of apple surface interested region, which were insect damage region, stem/calyx region and normal region, exhibted great differences in spectral reflectance at the 824 nm wavelength. So, the images of the 824 nm wavelength were named the feature images. Then, the feature images were processed by threshold segmentation, dilation, and erosion operation. A binarization image was obtained in the end. The binarization image was used to mask for apple hyperspectral image in order to remove noise of background on hyperspectral image. So a mask apple hyperspectral image was obtained. These processed hyperspectral images were carried by principal component analysis. The optimum PC1 image was chosen and processed by the maximum entropy threshold segmentation to get the insect damage region, stem/calyx region. According to interested region segmentation results, an interested region image was obtained from the PC1 image of each sample, with 80×60 pixels, 160×120 pix and 240×180 pix, respectively. Later, there were 4 the texture features of the gray level co-occurrence matrix (of energy, entropy, moment of inertia and correlation) of the insect damage region, stem/calyx region and the normal region on apples of the PC1 image. In addition, whether the spectral relative reflectance of the apple surface insect damage region, stem/calyx region and normal regions, was visible or near infrared region, showing some differences. So the two spectral features of the spectrum relative reflectivity at 646 nm and 824 nm wavelength were chosen, which had larger relative reflectance differences between the apple surface insect damage region, stem/calyx region and normal regions in the visible region and near infrared region. For faster and more accurate detection of the apple insect damage, the texture features and the spectral feature vectors were merged as input of Support Vector Machine (SVM), which is a recognition method of insect damage on apples. Finally, via a comparative analysis of recognition results with differences interested region size among 80×60 pix, 160×120 pix and 240×180 pix, we gained a result that the recognition result of apple insect of 160×120 pix interested region was the best. Through a comparative analysis of recognition with differences kernel function of SVM among linear kernel, polynomial kernel, rbf kernel and sigmoid kernel. The recognition effect of radial basis kernel function was the best, with the overall recognition rate of 97.8%. The testing results showed that hyperspectral imaging technology can be used for identification of insect damage and stem/calyx on apple fruit with quick, accurate and non-destructive detection and provided a theoretical basis for subsequent developing online apple quality detecting and grading system based on multispectral imaging technique.