Research on the feature model of kumquat oil gland based on OCT technology image processing algorithm

This study focuses on the synergistic effect of the development of oil glands in kumquats and the synthesis and accumulation of secondary metabolites. Through the distribution of oil glands in kumquat peels, it guides the cultivation of oily varieties, removes the bitter taste of peels, and improves the sustainable development of the kumquat industry. This study used Optical Coherence Tomography (OCT) technology to perform non-destructive scanning of kumquat peel, obtaining high-resolution images of its internal oil glands. Using the U-Net model of Convolutional Neural Network (CNN) to accurately segment OCT images of oil glands, extract the boundary and morphological features of oil glands, and perform 3D reconstruction on the segmented oil glands. Experiments have shown that the cross-sectional image of oil gland cells approximates an elliptical model, which can collect geometric parameters such as the long axis, short axis, and area of the oil gland. Three-dimensional reconstruction of the segmented oil gland reveals its cylindrical structure. The results indicate that the morphological parameters and characteristic distribution of oil glands will provide comprehensive, accurate, and efficient data support for the quality evaluation and grading of citrus, which will help optimize the quality control and scientific grading of kumquats, and provide reference for the sustainable development of kumquat industry.