基于HSV颜色空间的烟叶烘烤阶段判别模型研究

作者：汪伯军¹，郭保银¹，黄富饶¹，赵虎²，冯川²

（1. 中国烟草总公司重庆市公司烟叶分公司，重庆 400023；2. 西南大学工程技术学院，重庆 400716）

(1.Chongqing Tobacco Branch of China National Tobacco Corporation, Chongqing 400023; 2.School of Engineering and Technology, Southwest University, Chongqing 400716)

 

摘　要：【目的】烟叶烘烤的阶段判别对提升烘烤效率和品质具有重要意义，亟须实现烟叶烘烤阶段自动判别，减少人为影响，提高判别准确率。【方法】首先，使用图像处理技术将图像均衡化，再裁剪并剔除含背景信息多的图块，筛选出有效信息最多的图块；其次，提取烤烟图块的颜色特征，提取每一个图块的HSV三通道值，得到i项通道值；最后，使用基于阈值筛选的方法作为特征处理器，根据不同h、s、v值分类出烟叶烘烤的阶段，并将判断结果与实际情况进行对比验证。【结果】使用图像均衡化处理技术结合基于HSV颜色空间的特征提取算法，进行烟叶烘烤阶段判别，最终整体准确率达到90.64%，并且阶段3和阶段4的准确率达到了100%，效果非常理想。【结论】使用图像处理技术结合图像本身的颜色特征，能有效地判别烤烟的烘烤阶段，对判断烘烤进程、指导烘烤参数调节、提高烘烤品质、减少物料浪费和烘烤成本具有实际意义，有着广阔的应用前景。本研究为后续绕过深度学习等大算力算法但能提高实际应用效果方面的研究提供了方向。

关键词：烟叶烘烤；HSV颜色空间；特征提取；图像处理

 

Abstract: [Objective] The stage discrimination of tobacco curing is of great significance for improving the curing efficiency and quality. It is urgent to achieve automatic discrimination of tobacco curing stage, reduce human influence, and improve discrimination accuracy. [Method] Firstly, the image processing technology was used to equalize the image, then the image blocks with more background information were cut out, and the image blocks with the most effective information were select. Secondly, the color features of the flue-cured tobacco map blocks were extracted, and the HSV three-channel values of each block were extracted to obtain the i item channel values. Finally, the threshold-based screening method was used as the feature processor to classify the stage of flue-cured tobacco leaf according to different h, s and v values, and the judgment results are compared and verified with the actual situation. [Result] The image equalization processing technology combined with feature extraction algorithm based on HSV color space was used to distinguish the curing stage of tobacco leaves, and the overall accuracy reached 90.64%, and the accuracy of stage 3 and stage 4 reached 100%, the effect is very good. [Conclusion] Using image processing technology combined with the color characteristics of the image itself can effectively distinguish the curing stage of flue-cured tobacco, which has practical significance in judging the curing process, guiding the adjustment of curing parameters, improving curing quality, reducing material waste and curing costs, and has broad application prospects. This study provides a direction for subsequent research in bypassing large arithmetic algorithms such as deep learning but improving the effectiveness of practical applications.

Keywords: tobacco curing; HSV color space; feature extraction; the image processing