基于图像处理的烟叶水分含量快速预测
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摘要
利用图像处理技术进行了烤烟鲜烟叶水分含量的快速预测。使用1 300万～1 600万像素的数码相机或具有拍摄功能的手机采集不同生长时期不同烤烟品种的烟叶图像,利用MATLAB软件图像处理中的灰度直方图提取烟叶图像的灰度均值,建立了不同烟叶均值之间的关系图,根据均值与水分含量正相关的关系来快速预测烟叶水分含量。结果表明,成熟期烟叶,不同烤烟品种之间水分含量差别较大,相同品种之间水分含量也会有些差别,K326烟叶较云烟99在烘烤时需要的能量要多;生长期烟叶,不同烤烟品种所需水分是不同的,在旺长期,云烟87的需水量小于云烟99的需水量。
Digital image processing techniques were used to predict moisture content in tobacco leaves. In this study,the digital camera or mobile phone with 13-16 million pixels was used to capture the images of different tobacco varieties of tobacco leaves at different growing stage.The gray mean of leaf images were calculated by MATLAB,and the correlation relationship was set up between the gray mean of different leaves. Based on the positive correlation between mean and moisture content,moisture content of tobacco leaves was predicted rapidly. The results showed that,during the mature period,the moisture content of tobacco leaves differed greatly in different tobacco varieties,also differed in same tobacco variety,and K326 needed more energy for baking than Yunyan 99. During the growth period,water requirement for tobacco leaves differed in different tobacco varieties. Yunyan87 needed less water than that of Yunyan 99 in the prosperous period.
Digital image processing techniques were used to predict moisture content in tobacco leaves. In this study,the digital camera or mobile phone with 13-16 million pixels was used to capture the images of different tobacco varieties of tobacco leaves at different growing stage.The gray mean of leaf images were calculated by MATLAB,and the correlation relationship was set up between the gray mean of different leaves. Based on the positive correlation between mean and moisture content,moisture content of tobacco leaves was predicted rapidly. The results showed that,during the mature period,the moisture content of tobacco leaves differed greatly in different tobacco varieties,also differed in same tobacco variety,and K326 needed more energy for baking than Yunyan 99. During the growth period,water requirement for tobacco leaves differed in different tobacco varieties. Yunyan87 needed less water than that of Yunyan 99 in the prosperous period.
引文
[1]甘露萍,谢守勇,邹大军.基于计算机视觉的烤烟鲜烟叶含水量无损检测及MATLAB实现[J].西南大学学报,2009,31(7):166-170.
[2]劳东青,李发永.基于图像处理的葡萄叶片含水率诊断初探[J].节水灌溉,2015(3):5-7.
[3]徐腾飞,韩文霆,孙瑜.基于图像处理的玉米叶片含水率诊断方法研究[J].干旱地区农业研究,2013,1(1):95-100.
[4]孙瑞东,于海业,于常乐,等.基于图像处理的黄瓜叶片含水量无损检测研究[J].农机化研究,2008(7):87-89.
[5]张挣,倪红霞.精通matlab数字图像处理与识别[M].北京:人民邮电出版社,2013.
[6]刘益新,郭依正.灰度直方图特征提取的Matlab实现[J].电脑知识与技术,2009,5(32):9032-9034.
[7]张娟利,韩文霆,杨六生,等.基于图像处理的烤烟鲜烟叶含水率诊断研究[J].现代农业科技,2017(9):3-5.
[8]史龙飞.利用图像处理技术对烤烟烘烤过程主要化学成分预测研究[D].郑州:河南农业大学,2013.
[9]段史江,宋朝鹏,马力,等.基于图像处理的烘烤过程中烟叶含水量检测[J].西北农林科技大学学报(自然科学版),2012,40(5):74-80.
[10]刘朝营,许自成,闫铁军.机器视觉技术在烟草行业的应用状况[J].中国农业科技导报,2011,13(4):79-84.
[11]甘露萍.基于机器视觉技术的鲜烟叶含水量模型研究[D].重庆:西南大学,2009.
[2]劳东青,李发永.基于图像处理的葡萄叶片含水率诊断初探[J].节水灌溉,2015(3):5-7.
[3]徐腾飞,韩文霆,孙瑜.基于图像处理的玉米叶片含水率诊断方法研究[J].干旱地区农业研究,2013,1(1):95-100.
[4]孙瑞东,于海业,于常乐,等.基于图像处理的黄瓜叶片含水量无损检测研究[J].农机化研究,2008(7):87-89.
[5]张挣,倪红霞.精通matlab数字图像处理与识别[M].北京:人民邮电出版社,2013.
[6]刘益新,郭依正.灰度直方图特征提取的Matlab实现[J].电脑知识与技术,2009,5(32):9032-9034.
[7]张娟利,韩文霆,杨六生,等.基于图像处理的烤烟鲜烟叶含水率诊断研究[J].现代农业科技,2017(9):3-5.
[8]史龙飞.利用图像处理技术对烤烟烘烤过程主要化学成分预测研究[D].郑州:河南农业大学,2013.
[9]段史江,宋朝鹏,马力,等.基于图像处理的烘烤过程中烟叶含水量检测[J].西北农林科技大学学报(自然科学版),2012,40(5):74-80.
[10]刘朝营,许自成,闫铁军.机器视觉技术在烟草行业的应用状况[J].中国农业科技导报,2011,13(4):79-84.
[11]甘露萍.基于机器视觉技术的鲜烟叶含水量模型研究[D].重庆:西南大学,2009.