Citrus fruits, classified under the Rutaceae family and Citrus genus, are valued for their high nutritional content, attributed to their rich array of natural bioactive compounds. To ensure both quality and nutritional value, precise non-destructive testing methods are crucial. Among these, computer vision and spectroscopy technologies have emerged as key tools. This review examines the principles and applications of computer vision technologies—including traditional computer vision, hyperspectral, and multispectral imaging—as well as various spectroscopy techniques, such as infrared, Raman, fluorescence, terahertz, and nuclear magnetic resonance spectroscopy. Additionally, data fusion methods that integrate these technologies are discussed. The review explores innovative uses of these approaches in Citrus quality inspection and grading, damage detection, adulteration identification, and traceability assessment. Each technology offers distinct characteristics and advantages tailored to the specific testing requirements in Citrus production. Through data fusion, these technologies can be synergistically combined, enhancing the accuracy and depth of Citrus quality assessments. Future advancements in this field will likely focus on optimizing data fusion algorithms, selecting effective preprocessing and feature extraction techniques, and developing portable, on-site detection devices. These innovations will drive the Citrus industry toward increased intelligence and precision in quality control.

To address the limitations of traditional manual measurements of phenotypes, a study focused on the rapid acquisition of phenotypic parameters in wheat grains was conducted. This research introduced an index for bud-point determination, which offers valuable insights into breeding selection, optimizing sowing and growth direction for grains, and assessing seed vitality. A grain collection device utilizing a linear array camera was designed and constructed, accompanied by custom software to simplify device operation. Image processing was performed using Halcon image, enabling the development and implementation of a system for extracting key phenotypic parameters. The preprocessing method involves isolating wheat grain regions, combined with S channel extraction, global thresholding, and opening and closing operations to achieve robust segmentation. Based on this preprocessing method, a phenotypic analysis was performed by evaluating the geometric characteristics of wheat grains. The process for extracting phenotypic characteristics and determining the bud point was subjected to experimental analysis. The results demonstrated high correlation and accuracy. The errors estimating the comprehensive grain length of five wheat varieties using the extraction algorithm developed in this study, the determination coefficient and root mean square error indices, were 0.986 and 0.0887, respectively, compared with manual measurements. Similarly, for grain width, the determination coefficient and RMSE were 0.9505 and 0.0541; for grain weight, these values were 0.7635 and 3.8329, respectively. Furthermore, the algorithm achieved a bud point direction determination accuracy rate of 97.5%. The mean error in the positioning accuracy of germination points was within 0.10 mm, and the mean angular error was maintained within ±3 degrees. The phenotype analysis of wheat using the image processing techniques presented in this study shows strong consistency with manual detection methods, providing valuable quantitative data to assist in breeding selection and accelerate genetic improvement practices.