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Fungal rot is the most serious defect in post-harvest citrus, and the timely detection and removal of early rotten citrus are particularly crucial in reducing economic losses. Imaging technology under structured illumination is a promising method to fruit surface defects. This study aims to assess the feasibility of combining the structured-illumination reflectance imaging (SIRI) system with deep learning technology for the identification and segmentation of early rot in citrus. Phase-shifted images of oranges were acquired at four spatial frequencies and demodulated to obtain direct component (DC) and amplitude component (AC) images. The optimal spatial frequency for detecting rot was determined to be 0.20 cycles mm−1 based on the contrast index between the decay and sound areas in the images. Then, the AC images are subjected to brightness correction and augmentation. Three segmentation methods, global thresholding, watershed segmentation and Unet were used to segment the rotten areas in the images. Unet achieved optimal results on AC images, with an overall accuracy of 99.4 % and an IoU of 0.903. The gradient-weighted class activation mapping (Grad-CAM) was used to visualize the areas recognized by Unet for orange rot, yielding satisfactory results. This study effectively demonstrated the defect recognition capability of SIRI combined with deep learning and providing a reliable solution for early decayed detection in oranges. • Detected early decayed oranges by structured-illumination reflectance imaging. • Three methods were employed to segment the decayed areas in the images. • Unet achieved optimal results on amplitude component images. [ABSTRACT FROM AUTHOR]

• Common defects on mandarins were detected by Vis-NIR hyperspectral imaging. • Principal component analysis was used to reduce hyperspectral data dimensionality. • Two characteristic wavelength images at 680 nm and 715 nm were selected. • Multispectral detection algorithm was developed based on band ratio and PCA method. • 96.63% detection accuracy was obtained for all tested samples with five peel types. The presence of surface defects is one of the most influential factors in the quality and price of fresh fruit because consumers usually associate quality with a good appearance and without skin defects. Therefore, one of the main purposes of automatic detection of fruit quality is to differentiate between defective ones from sound fruits. However, the detection of defective fruits has always been a challenging task, especially the simultaneous detection of multiple types of defects. This work focuses on the development of multispectral image classification algorithm for detecting the common defects on mandarins based on the visible-near infrared (Vis-NIR) hyperspectral imaging technique. 'Nanfeng' mandarins with sound peel and four types of defects (i.e., anthracnose, scarring, decay and thrips scarring) were studied. Principal component analysis (PCA) was used to reduce hyperspectral data dimensionality with the goal of selecting several wavelengths that could be potentially used in an in-line multispectral imaging system. Two characteristic wavelength images at 680 nm and 715 nm in the visible spectral region were selected, and then the second principal component image (PC-2) and ratio image (Q 680/715) based on these two characteristic wavelengths were used for defect detection and stem-end identification, respectively. Finally, the detection algorithm of defects was developed based on PC-2 image and ratio image (Q 680/715) coupled with a simple thresholding method. For the investigated 356 independent test samples, classification accuracy of 96.63% indicated that the proposed multispectral image algorithm was effective for distinguishing between sound and defective 'Nanfeng' mandarins. Only two wavelength images were used in the algorithm, which was very helpful to develop a fast multispectral imaging system for on-line grading of mandarins. [ABSTRACT FROM AUTHOR]

• Fast detection of early decay in citrus still remains one of the major problems. • Seven effective wavelength images were selected to obtain multispectral PC images. • BEMD was used to remove noise in the multispectral PC image and reconstruct image. • An improved watershed segmentation method was proposed to segment decay regions. • Methodology was effective with the decayed oranges detection accuracy of 97.3%. Detection of early decay caused by fungal infections in citrus fruit still remains one of the major problems in the post-harvest processing and automatic quality grading. A new combination algorithm by merging multispectral principal component image, bi-dimensional empirical mode decomposition and image reconstruction as well as improved watershed segmentation was proposed to detect the early decay in oranges. Segmented principal component analysis based on three wavelength regions including visible and short wavelength near-infrared (500–1050 nm), visible (500–780 nm) and near-infrared (781–1050 nm) was performed to determine the optimal principal component (PC) image that was used to extract the effective wavelength images by weighting coefficient analysis. Seven wavelength images in the spectral region of 500–1050 nm were finally determined to build the multispectral PC images. The bi-dimensional empirical mode decomposition (BEMD) was used to remove noise in the multispectral PC images and further reconstruct images. An improved watershed segmentation method with morphological gradient reconstruction, marker extraction as well as image amendment, was proposed to segment decay regions in fruit by using the reconstructed multispectral PC images. All samples including 220 each of decayed and sound fruit were utilized to assess classification ability of the proposed combination algorithm. The results indicated that identification accuracies of decayed and sound fruit were 97.3% and 100%, respectively. The multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method can be used as an effective tool for detection of early decayed oranges, and it was also promising for development of a fast and low-cost multispectral imaging system. [ABSTRACT FROM AUTHOR]