马铃薯薄片干燥过程形态变化三维成像
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摘要
为研究马铃薯薄片在干燥过程中形态变化规律,该文利用Kinect传感器搭建了图像采集平台,研究其在不同干燥温度下(50、60、70、80℃)的形态变化规律。通过图像采集平台获取马铃薯薄片深度图像和彩色图像,利用彩色图像确定感兴趣区域,对对应区域的深度图像进行灰度值拉伸、阈值分割、边缘去噪处理,进而提取特征,计算出正投影面积的收缩率、深度均值及标准差,以表征马铃薯干燥过程中表面卷曲及平整度等形态指标的变化规律。对不同干燥时间点马铃薯片进行三维图形显示可观察其变化规律明显。统计结果表明:低温(50、60℃)与高温(70、80℃)对马铃薯薄片干燥时的收缩率、卷曲程度具有显著影响(P<0.05)。50℃时收缩率为54.97%,80℃时收缩率升高为64.55%;干燥温度与马铃薯片卷曲程度呈先升后降的关系,60℃时卷曲度最大,其深度均值为27.81 mm,80℃时降低到18.86 mm。而四组温度下,马铃薯薄片的平整度具有显著性差异(P<0.05),50℃时马铃薯片深度值的标准差为7.99 mm,80℃时降低至5.71mm,说明平整度随着干燥温度升高而增加。该研究可为马铃薯薄片干燥过程中形态变化的检测提供参考,同时为干燥工艺的智能化控制提供技术依据。
Drying is an important method for agricultural products processing. It can reduce the moisture content of agricultural products to a certain extent and extend the shelf life. But irregular deformation resulting from drying process will cause inconvenience for subsequent processing. In order to study the regularity of deformation during the drying process of potato slices, we built an image acquisition platform based on the Kinect sensor. Firstly, we verified the accuracy of the depth detection of the Kinect image acquisition platform by cubes with 5, 8, and 10 mm sides. The results showed that the accuracy can reach 2 mm. Secondly, we selected potato as the research object and dried them using a tunnel hot air dryer. Controlled potato slice thickness was 1 mm, drying room humidity was 15%, hot wind speed was 3 m/s to study the deformation regularity of potato slice at different temperatures(50, 60, 70, 80 ℃). After the drying process began, the potato slices were taken out of the drying chamber and put on the Kinect image acquisition platform to acquire depth and color images, then weighed every 10 minutes. We used the Kinect SDK function to achieve a one-to-one correspondence between color images and depth images. According to the position of the material in the color image, the region where the potato slices were located of interest was established, and the potato slices were located at the same coordinate position in the depth image. Gray value stretching, threshold segmentation, and edge denoising were performed on the corresponding region of depth images. Then feature extraction was used to distinguish every potato slice and calculate its shrinkage rate, mean depth values and standard deviation. The mean depth value can reflect the curling of the potato slices during the drying process. The shrinkage rate could reflect the shrinkage characteristics of the potato slices in the drying process. And the standard deviation of the depth value could reflect the surface flatness of potato slices in drying process. Then we drew the curve of dry basis moisture content and the three parameters under different temperature conditions, which could be more directly to observe the effect of temperature on the deformation of potato slices during drying. The results showed that temperature had a significant effect on the shrinkage, surface curl, and surface flatness of the potato slices in the drying process(P<0.05). With the increase of temperature, the shrinkage of potato slices increased gradually. At 50 ℃, the shrinkage rate was 54.97%. When the temperature rose to 80 ℃, the shrinkage rate increased to 64.55%. With the increase of temperature, the variation of curl and flatness of the potato chips was small. The mean depth value of the potato slices was 27.81 mm at 60 ℃, and it decreased to 18.86 mm at 80 ℃. At 50 ℃, the standard deviation of potato slices depth was 7.99 mm, which decreased to 5.71 mm at 80 ℃. Finally, using MATLAB software to display three-dimensional graphics of potato slices in five different time periods, the surface deformation of potato slices could be clearly observed. It was illustrated that the Kinect image acquisition platform could be applied to the study of deformation regularity in the drying process of potato slices, and provide technical basis for intelligent control of the drying process.
Drying is an important method for agricultural products processing. It can reduce the moisture content of agricultural products to a certain extent and extend the shelf life. But irregular deformation resulting from drying process will cause inconvenience for subsequent processing. In order to study the regularity of deformation during the drying process of potato slices, we built an image acquisition platform based on the Kinect sensor. Firstly, we verified the accuracy of the depth detection of the Kinect image acquisition platform by cubes with 5, 8, and 10 mm sides. The results showed that the accuracy can reach 2 mm. Secondly, we selected potato as the research object and dried them using a tunnel hot air dryer. Controlled potato slice thickness was 1 mm, drying room humidity was 15%, hot wind speed was 3 m/s to study the deformation regularity of potato slice at different temperatures(50, 60, 70, 80 ℃). After the drying process began, the potato slices were taken out of the drying chamber and put on the Kinect image acquisition platform to acquire depth and color images, then weighed every 10 minutes. We used the Kinect SDK function to achieve a one-to-one correspondence between color images and depth images. According to the position of the material in the color image, the region where the potato slices were located of interest was established, and the potato slices were located at the same coordinate position in the depth image. Gray value stretching, threshold segmentation, and edge denoising were performed on the corresponding region of depth images. Then feature extraction was used to distinguish every potato slice and calculate its shrinkage rate, mean depth values and standard deviation. The mean depth value can reflect the curling of the potato slices during the drying process. The shrinkage rate could reflect the shrinkage characteristics of the potato slices in the drying process. And the standard deviation of the depth value could reflect the surface flatness of potato slices in drying process. Then we drew the curve of dry basis moisture content and the three parameters under different temperature conditions, which could be more directly to observe the effect of temperature on the deformation of potato slices during drying. The results showed that temperature had a significant effect on the shrinkage, surface curl, and surface flatness of the potato slices in the drying process(P<0.05). With the increase of temperature, the shrinkage of potato slices increased gradually. At 50 ℃, the shrinkage rate was 54.97%. When the temperature rose to 80 ℃, the shrinkage rate increased to 64.55%. With the increase of temperature, the variation of curl and flatness of the potato chips was small. The mean depth value of the potato slices was 27.81 mm at 60 ℃, and it decreased to 18.86 mm at 80 ℃. At 50 ℃, the standard deviation of potato slices depth was 7.99 mm, which decreased to 5.71 mm at 80 ℃. Finally, using MATLAB software to display three-dimensional graphics of potato slices in five different time periods, the surface deformation of potato slices could be clearly observed. It was illustrated that the Kinect image acquisition platform could be applied to the study of deformation regularity in the drying process of potato slices, and provide technical basis for intelligent control of the drying process.
引文
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[8]沈跃,徐慧,刘慧,等.基于K-means和近邻回归算法的Kinect植株深度图像修复[J].农业工程学报,2016,32(19):188-194.Shen Yue,Xu Hui,Liu Hui,et,al.Kinect scanning plant depth image restoration based on K-means and K-nearest neighbor algorithms[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(19):188-194.(in Chinese with English abstract)
[9]邹广群.基于TOF相机的深度图增强算法研究[D].青岛:青岛大学,2016.
[10]赵志平,陈雷月.基于深度传感器图像分割技术的研究[J].信息技术,2014(1):109-112.Zhao Zhiping,Chen Leiyue.Research on image segment technique based on Kinect[J].Information Technology,2014(1):109-112.(in Chinese with English abstract)
[11]许常蕾,王庆,陈洪,等.基于体感交互的仿上肢采摘机器人系统设计与仿真[J].农业工程学报,2017,33(增刊1):49-55.Xu Changlei,Wang Qing,Chen Hong,et al.Design and simulation of artificial limb picking robot based on somatosensory interaction[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(Supp.1):49-55.(in Chinese with English abstract)
[12]Meng Ming,Yang Fangbo,She Qingshan,et al.Human motion detection based on the depth image of Kinect[J].Chinese Journal of Scientific Instrument,2015,36(2):386-393.
[13]Harte J M,Golby C K,Acosta J,et al.Chest wall motion analysis in healthy volunteers and adults with cystic fibrosis using a novel Kinect-based motion tracking system[J].Medical&Biological Engineering&Computing,2016,54(11):1631-1640.
[14]Sun Y,Li C,Li G,et al.Gesture recognition based on Kinect and sEMG signal fusion[J].Mobile Networks&Applications,2018(1):1-9.
[15]Rosa-Pujazón A,Barbancho I,Tardón L J,et al.Fast-gesture recognition and classification using Kinect:An application for a virtual reality drumkit[J].Multimedia Tools&Applications,2016,75(14):8137-8164.
[16]Gianaria E,Grangetto M,Balossino N.Kinect-based gait analysis for people recognition over time[C]//International Conference on Image Analysis and Processing.Catania:Springer,2017:648-658.
[17]付代昌,徐立鸿,李大威,等.基于Kinect的温室番茄盆栽茎干检测与分割[J].现代农业科技,2014(3):336-338.
[18]沈跃,徐慧,刘慧,等.基于Kinect传感器的温室植株绿色与深度检测方法[J].中国农机化学报,2016,37(8):155-161.
[19]沈跃,朱嘉慧,刘慧,等.基于深度和彩色双信息特征源的Kinect植物图像拼接[J].农业工程学报,2018,34(5):176-182.Shen Yue,Zhu Jiahui,Liu Hui,et,al.Plant image mosaic based on depth and color dual information feature source from Kinect[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(5):176-182.(in Chinese with English abstract)
[20]肖珂,高冠东,马跃进.基于Kinect视频技术的葡萄园农药喷施路径规划算法[J].农业工程学报,2017,33(24):192-199.Xiao Ke,Gao Guandong,Ma Yuejin.Pesticide spraying route planning algorithm for grapery based on Kinect video technique[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(24):192-199.(in Chinese with English abstract)
[21]闻陶,朱跃钊,孙庆梅,等.马铃薯吸附干燥特性及模型拟合[J].生物加工过程,2006,4(3):56-61.
[22]Hafezi N,Sheikhdavoodi M J,Sajadiye S M,et al.Shrinkage characteristic of potato slices based on computer vision[J].Agricultural Engineering International Cigr Journal,2015,17(3):287-295.
[23]夏春华,施滢,尹文庆.基于TOF深度传感的植物三维点云数据获取与去噪方法[J].农业工程学报,2018,34(6):168-174.Xia Chunhua,Shi Ying,Yin Wenqing.Obtaining and denoising method of three-dimensional point cloud data of plants based on TOF depth sensor[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(6):168-174.(in Chinese with English abstract)
[24]Takenaka H,Wada T,Oike H,et al.Bilateral filtering based depth image correction consistent with color image captured by Kinect[J].Technical Report of Ieice Prmu,2012,112:311-316.
[25]徐胜勇,黄伟军,周俊,等.使用Kinect传感器的油菜叶片面积测量方法[J].中国油料作物学报,2017,39(1):55-59.
[26]刘小丹,张淑娟,贺虎兰,等.红枣微波-热风联合干燥特性及对其品质的影响[J].农业工程学报,2012,28(24):280-286.Liu Xiaodan,Zhang Shujuan,He Hulan,et al.Drying characteristics and its effects on quality of jujube treated by combined microwave-hot-air drying[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(24):280-286.(in Chinese with English abstract)
[27]朱文魁,徐德龙,周雅宁,等.基于数字图像分析法的片烟干燥收缩特性研究[J].河南农业科学,2014,43(7):160-164.
[28]徐娓,丁静,赵义,等.多孔物料干燥中物料体积的收缩特性[J].华南理工大学学报:自然科学版,2006,34(8):61-65.
[29]Wang J,Law C L,Nema P K,et al.Pulsed vacuum drying enhances drying kinetics and quality of lemon slices[J].Journal of Food Engineering,2018,224:129-138.
[30]王丽红,高振江,张茜,等.加工番茄收缩特性的试验研究[J].食品科技,2011(8):102-105.
[31]Mayor L,Sereno A M.Modelling shrinkage during convective drying of food materials:A review[J].Journal of Food Engineering,2004,61(3):373-386.
[32]沈文超.不规则扁平粒状物表面平整度识别与分选方法的研究[D].苏州:苏州大学,2010.
[2]吴海虹,朱道正,卞欢,等.农产品干燥技术发展现状[J].现代农业科技,2016(14):279-281.
[3]白竣文,田潇瑜,刘宇婧,等.大野芋薄层干燥特性及收缩动力学模型研究[J].中国食品学报,2018,18(4):124-130.
[4]Nadian M H,Rafiee S,Aghbashlo M,et al.Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying[J].Food&Bioproducts Processing,2015,94:263-274.
[5]Sampson D J,Chang Y K,Rupasinghe H P V,et al.Adual-view computer-vision system for volume and image texture analysis in multiple apple slices drying[J].Journal of Food Engineering,2014,127(4):49-57.
[6]Ortiz-García-Carrasco B,Ya?ez-Mota E,Pacheco-Aguirre FM,et al.Drying of shrinkable food products:Appraisal of deformation behavior and moisture diffusivity estimation under isotropic shrinkage[J].Journal of Food Engineering,2015,144:138-147.
[7]Onwude D I,Hashim N,Abdan K,et al.Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato(Ipomoea batatas,L.)during drying[J].Computers&Electronics in Agriculture,2018,150:178-187.
[8]沈跃,徐慧,刘慧,等.基于K-means和近邻回归算法的Kinect植株深度图像修复[J].农业工程学报,2016,32(19):188-194.Shen Yue,Xu Hui,Liu Hui,et,al.Kinect scanning plant depth image restoration based on K-means and K-nearest neighbor algorithms[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(19):188-194.(in Chinese with English abstract)
[9]邹广群.基于TOF相机的深度图增强算法研究[D].青岛:青岛大学,2016.
[10]赵志平,陈雷月.基于深度传感器图像分割技术的研究[J].信息技术,2014(1):109-112.Zhao Zhiping,Chen Leiyue.Research on image segment technique based on Kinect[J].Information Technology,2014(1):109-112.(in Chinese with English abstract)
[11]许常蕾,王庆,陈洪,等.基于体感交互的仿上肢采摘机器人系统设计与仿真[J].农业工程学报,2017,33(增刊1):49-55.Xu Changlei,Wang Qing,Chen Hong,et al.Design and simulation of artificial limb picking robot based on somatosensory interaction[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(Supp.1):49-55.(in Chinese with English abstract)
[12]Meng Ming,Yang Fangbo,She Qingshan,et al.Human motion detection based on the depth image of Kinect[J].Chinese Journal of Scientific Instrument,2015,36(2):386-393.
[13]Harte J M,Golby C K,Acosta J,et al.Chest wall motion analysis in healthy volunteers and adults with cystic fibrosis using a novel Kinect-based motion tracking system[J].Medical&Biological Engineering&Computing,2016,54(11):1631-1640.
[14]Sun Y,Li C,Li G,et al.Gesture recognition based on Kinect and sEMG signal fusion[J].Mobile Networks&Applications,2018(1):1-9.
[15]Rosa-Pujazón A,Barbancho I,Tardón L J,et al.Fast-gesture recognition and classification using Kinect:An application for a virtual reality drumkit[J].Multimedia Tools&Applications,2016,75(14):8137-8164.
[16]Gianaria E,Grangetto M,Balossino N.Kinect-based gait analysis for people recognition over time[C]//International Conference on Image Analysis and Processing.Catania:Springer,2017:648-658.
[17]付代昌,徐立鸿,李大威,等.基于Kinect的温室番茄盆栽茎干检测与分割[J].现代农业科技,2014(3):336-338.
[18]沈跃,徐慧,刘慧,等.基于Kinect传感器的温室植株绿色与深度检测方法[J].中国农机化学报,2016,37(8):155-161.
[19]沈跃,朱嘉慧,刘慧,等.基于深度和彩色双信息特征源的Kinect植物图像拼接[J].农业工程学报,2018,34(5):176-182.Shen Yue,Zhu Jiahui,Liu Hui,et,al.Plant image mosaic based on depth and color dual information feature source from Kinect[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(5):176-182.(in Chinese with English abstract)
[20]肖珂,高冠东,马跃进.基于Kinect视频技术的葡萄园农药喷施路径规划算法[J].农业工程学报,2017,33(24):192-199.Xiao Ke,Gao Guandong,Ma Yuejin.Pesticide spraying route planning algorithm for grapery based on Kinect video technique[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(24):192-199.(in Chinese with English abstract)
[21]闻陶,朱跃钊,孙庆梅,等.马铃薯吸附干燥特性及模型拟合[J].生物加工过程,2006,4(3):56-61.
[22]Hafezi N,Sheikhdavoodi M J,Sajadiye S M,et al.Shrinkage characteristic of potato slices based on computer vision[J].Agricultural Engineering International Cigr Journal,2015,17(3):287-295.
[23]夏春华,施滢,尹文庆.基于TOF深度传感的植物三维点云数据获取与去噪方法[J].农业工程学报,2018,34(6):168-174.Xia Chunhua,Shi Ying,Yin Wenqing.Obtaining and denoising method of three-dimensional point cloud data of plants based on TOF depth sensor[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(6):168-174.(in Chinese with English abstract)
[24]Takenaka H,Wada T,Oike H,et al.Bilateral filtering based depth image correction consistent with color image captured by Kinect[J].Technical Report of Ieice Prmu,2012,112:311-316.
[25]徐胜勇,黄伟军,周俊,等.使用Kinect传感器的油菜叶片面积测量方法[J].中国油料作物学报,2017,39(1):55-59.
[26]刘小丹,张淑娟,贺虎兰,等.红枣微波-热风联合干燥特性及对其品质的影响[J].农业工程学报,2012,28(24):280-286.Liu Xiaodan,Zhang Shujuan,He Hulan,et al.Drying characteristics and its effects on quality of jujube treated by combined microwave-hot-air drying[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(24):280-286.(in Chinese with English abstract)
[27]朱文魁,徐德龙,周雅宁,等.基于数字图像分析法的片烟干燥收缩特性研究[J].河南农业科学,2014,43(7):160-164.
[28]徐娓,丁静,赵义,等.多孔物料干燥中物料体积的收缩特性[J].华南理工大学学报:自然科学版,2006,34(8):61-65.
[29]Wang J,Law C L,Nema P K,et al.Pulsed vacuum drying enhances drying kinetics and quality of lemon slices[J].Journal of Food Engineering,2018,224:129-138.
[30]王丽红,高振江,张茜,等.加工番茄收缩特性的试验研究[J].食品科技,2011(8):102-105.
[31]Mayor L,Sereno A M.Modelling shrinkage during convective drying of food materials:A review[J].Journal of Food Engineering,2004,61(3):373-386.
[32]沈文超.不规则扁平粒状物表面平整度识别与分选方法的研究[D].苏州:苏州大学,2010.