基于图像分割与融合特征的黄瓜叶片含水量分析方法
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摘要
植物叶片的含水量能反映植物的生长状况。为了实现通过图像分析技术对植物叶片的含水量进行无损检测,以黄瓜叶片为研究对象,针对不同发育时期的黄瓜植株,分批次采集植物整株图像。使用GrabCut算法分割得到目标叶片图像,融合计算灰度统计特征及纹理特征,与叶片含水量建立回归模型。结果表明:建立的回归模型R~2为0. 8358,对于不同生育期的黄瓜叶片的含水量均有较好的预测能力,两批测试数据的平均相对误差为10. 88%和7. 98%。该模型可以用于黄瓜叶片含水量无损检测,可与高通量表型平台相结合,提高黄瓜叶片含水量变化连续监测的精确度。
The leaf water content of plants responds to the growth of the plant. In this paper,a method of nondestructive detection of the leaf water content by image analysis was proposed. Taking the cucumber leaves as the research object,the whole plant images were collected in batches of cucumber plants at different developmental stages. The target leaf images were obtained by GrabCut algorithm,and the color,gray statistics and texture features were calculated. Then,a regression model with the leaf water content was established. The results showed that the established regression model with R~2 kept good predictive ability for the water content of cucumber leaves at different growth stages. The average relative errors between the two batches of test data were10. 88% and 7. 98%. The model could be used for nondestructive testing of cucumber leaf water content and could be combined with high-throughput phenotyping platform to improve the accuracy of continuous monitoring of cucumber leaf water content.
The leaf water content of plants responds to the growth of the plant. In this paper,a method of nondestructive detection of the leaf water content by image analysis was proposed. Taking the cucumber leaves as the research object,the whole plant images were collected in batches of cucumber plants at different developmental stages. The target leaf images were obtained by GrabCut algorithm,and the color,gray statistics and texture features were calculated. Then,a regression model with the leaf water content was established. The results showed that the established regression model with R~2 kept good predictive ability for the water content of cucumber leaves at different growth stages. The average relative errors between the two batches of test data were10. 88% and 7. 98%. The model could be used for nondestructive testing of cucumber leaf water content and could be combined with high-throughput phenotyping platform to improve the accuracy of continuous monitoring of cucumber leaf water content.
引文
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[13]陈美龙,戴声奎.基于GLCM算法的图像纹理特征分析[J].通信技术,2012,45(2):108-111.
[14]周田田.基于机器视觉小麦叶片含水量检测研究[D].泰安:山东农业大学,2017.
[15]陶强,刘莉.基于双区域演化水平集的图像分割方法[J].电子测量技术,2016,39(9):91-107.
[16]王娟.基于计算机视觉的棉花干旱诊断研究[D].石河子:石河子大学,2014.
[17]孙俊,宋佳,武小红,等.基于改进Otsu算法的生菜叶片图像分割方法[J].江苏大学学报(自然科学版),2018(2):179-184.
[18]黄南,王若兰,岳佳.基于RGB图像特征的大豆含水量快速测定系统研究[J].粮油食品科技,2016,24(1):106-111.
[2]BAEZA P,SANCHEZ-DE-MIGUEL P,CENTENO A,et al. Water relations between leaf water potential,photosynthesis and agronomic vine response as a tool for establishing thresholds in irrigation scheduling[J]. Scientia Horticulturae,2007,114(3):151-158.
[3]MEYER G E,STEPANEK A,SHELTON D P,et al. Electronic image analysis of crop residue cover on soil[J]. Transactions of the ASAE,1988,31(3):968-973.
[4]SPALDING E P,MILLER N D. Image analysis is driving a renaissance in growth measurement[J]. Current Opinion in Plant Biology,2013,16(1):100-104.
[5]OMASA K,Takayama K. Simultaneous measurement of stomatal conductance,non-photochemical quenching,and photochemical yield of photosystem II in intact leaves by thermal and chlorophyll fluorescence imaging[J]. Plant and Cell Physiology,2003,44(12):1290-1300.
[6]GARCIA-RUIZ F,SANKARAN S,MAJA J M,et al. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees[J]. Computers and Electronics in Agriculture,2013,91:106-115.
[7]NUSKE S,ACHAR S,BATES T,et al. Yield estimation in vineyards by visual grape detection[C]∥Intelligent Robots and Systems(IROS),2011 IEEE/RSJ International Conference on. IEEE,2011:2352-2358.
[8]蔡鸿昌,崔海信,宋卫堂,等.黄瓜初花期叶片光合色素含量与颜色特征的初步研究[J].农业工程学报,2006,22(9):34-38.
[9]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.
[10]杨春合,江朝晖,杨宝华,等.基于移动互联网图像处理模式的作物叶片含水量检测研究[J].浙江农业学报,2015,27(10):1835-1840.
[11]ROTHER C,KOLMOGOROV V,BLAKE A. Grabcut:Interactive foreground extraction using iterated graph cuts[C]. ACM transactions on graphics(TOG). ACM,2004,23(3):309-314.
[12]张明军,俞文静,吴婕,等.一种Grab Cut分割边缘的优化方法[J].西南师范大学学报(自然科学版),2017,42(9):47-52.
[13]陈美龙,戴声奎.基于GLCM算法的图像纹理特征分析[J].通信技术,2012,45(2):108-111.
[14]周田田.基于机器视觉小麦叶片含水量检测研究[D].泰安:山东农业大学,2017.
[15]陶强,刘莉.基于双区域演化水平集的图像分割方法[J].电子测量技术,2016,39(9):91-107.
[16]王娟.基于计算机视觉的棉花干旱诊断研究[D].石河子:石河子大学,2014.
[17]孙俊,宋佳,武小红,等.基于改进Otsu算法的生菜叶片图像分割方法[J].江苏大学学报(自然科学版),2018(2):179-184.
[18]黄南,王若兰,岳佳.基于RGB图像特征的大豆含水量快速测定系统研究[J].粮油食品科技,2016,24(1):106-111.