基于图像处理的食品质量检测与分级技术研究

Research on Food Quality Inspection and Grading Technology Based on Image Processing

针对食品质量检测与分级领域传统人工检测方法存在的耗时费力、易受主观因素影响等问题,提出加快图像处理技术在食品表面瑕疵与异物检测、成熟度评估以及营养成分预测中的应用。通过图像预处理、特征提取以及机器学习或深度学习模型的结合,实现缺陷的自动检测与分类、食品成熟度的客观量化评估以及营养成分的快速预测。通过研究食品分级技术的分级标准设定、自动分级系统设计以及性能评估与优化,分析光照变化、复杂背景处理以及大规模数据处理等关键技术挑战,提出相应的解决方案与策略,以期提高基于图像处理的食品质量检测与分级技术的准确性和实用性。

Addressing the issues of time-consuming and labor-intensive traditional manual inspection methods,as well as their susceptibility to subjective inf luences in the field of food quality inspection and grading,this paper proposes to accelerate the application of image processing technology in the detection of food surface defects and foreign bodies,maturity assessment and nutritional composition prediction.By integrating image preprocessing,feature extraction,and machine learning or deep learning models,we achieve automatic detection and classification of defects,objective quantitative assessment of food maturity,and rapid estimation of nutritional content.By studying the grading standard setting,automatic grading system design,performance evaluation and optimization of food grading technology,analyzing key technical challenges such as lighting changes,complex background processing,and large-scale data processing,corresponding solutions and strategies are proposed to improve the accuracy and practicality of image processing based food quality detection and grading technology.