[1]刘连忠,李孟杰,宁井铭.基于改进SLIC的光照干扰下茶树冠层图像分割[J].江苏农业学报,2020,(04):1022-1027.[doi:doi:10.3969/j.issn.1000-4440.2020.04.030]
　LIU Lian-zhong,LI Meng-jie,NING Jing-ming.Segmentation of tea plant canopy image under light interference based on improved SLIC[J].,2020,(04):1022-1027.[doi:doi:10.3969/j.issn.1000-4440.2020.04.030]
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基于改进SLIC的光照干扰下茶树冠层图像分割()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2020年04期
页码:
1022-1027
栏目:
园艺
出版日期:
2020-08-31

文章信息/Info

Title:
Segmentation of tea plant canopy image under light interference based on improved SLIC
作者:
刘连忠1; 李孟杰1; 宁井铭2
(1.安徽农业大学信息与计算机学院，安徽合肥230036;2.茶树生物学与资源利用国家重点实验室，安徽合肥230036)
Author(s):
LIU Lian-zhong1; LI Meng-jie1; NING Jing-ming2
(1.School of Information and Computer, Anhui Agricultural University, Hefei 230036, China;2.State Key Laboratory of Tea Plant Biology and Resource Utilization, Hefei 230036, China)
关键词:
农作物图像; 图像分割; 超像素; 图像分类; 特征参数
Keywords:
crop images; image segmentation; superpixels; image classification; feature parameter
分类号:
S126
DOI:
doi:10.3969/j.issn.1000-4440.2020.04.030
文献标志码:
A
摘要:
针对自然环境下获取农作物图像时极易受到光照干扰的问题，提出一种改进的简单线性迭代聚类(SLIC)方法，以[L*,R,G-S,x,y]作为聚类向量对茶树冠层图像进行超像素分割，提取超像素块的R、G、B、H、S、V、L*、a*、b*、熵、能量、对比度、逆差矩等13个图像特征参数；将超像素块分为正常区域、反光区域、背景3类，分别选择线性、多项式和RBF核函数的SVM进行分类，得到仅包含正常区域的茶树冠层图像，进而提取正常区域的图像特征参数。试验结果表明，在光照变化情况下，改进的SLIC与RBF-SVM结合得到的图像特征最为稳定。
Abstract:
In order to solve the problem that crop images in natural environment were easily affected by the change of light, the improved simple linear iterative clustering(SLIC) method was proposed. Using [L*,R,G-S,x,y] as clustering vector, the superpixel segmentation of the tea plant canopy image was carried out. Then 13 image feature parameters such as R, G, B, H, S, V, L*, a*, b*, entropy, energy, contrast and inverse difference moment of the super-pixel block were extracted. The super-pixel blocks were divided into three categories: normal area, reflective area and background, and classified by support vector machine(SVM) with linear, polynomial and RBF kernel functions separately. Finally, the canopy image of tea plant containing only the normal area was obtained, and the image feature parameters of the normal area were extracted. The results showed that the image feature parameters obtained by the improved SLIC and RBF-SVM were the most stable under the condition of light interference.

参考文献/References:

[1]林开颜,徐立鸿,吴军辉.计算机视觉技术在作物生长监测中的研究进展[J].农业工程学报,2004,20(2):279-283.
[2]李朝东,崔国贤,盛畅,等.计算机视觉技术在农业领域的应用[J].农机化研究, 2009(12):234-238.
[3]李晓斌,郭玉明.机器视觉高精度测量技术在农业工程中的应用[J].农机化研究,2012,34(5):7-11.
[4]陈梅香,刘蒙蒙,赵丽,等.基于机器视觉的设施农业害虫监测技术研究进展与展望[J].农业工程技术,2017,37(31):10-15.
[5]郑纪业,阮怀军,封文杰,等.农业物联网体系结构与应用领域研究进展[J].中国农业科学,2017,50(4):657-668.
[6]REVATHI P,HEMALATHA M.Classification of cotton leaf spot diseases using image processing edge detection techniques[C]//IEEE. 2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET).Tiruchirappalli:IEEE,2012:169-173.
[7]SILVA F F, LUZ P H C, ROMUALDO L M, et al. A diagnostic tool for magnesium nutrition in maize based on image analysis of different leaf sections[J].Crop Science,2014,54(2):738-745.
[8]张珏,田海清,李哲,等.基于数码相机图像的甜菜冠层氮素营养监测[J].农业工程学报,2018,34(1):157-163.
[9]吴雪梅,张富贵,吕敬堂.基于图像颜色信息的茶叶嫩叶识别方法研究[J].茶叶科学, 2013(6):98-103.
[10]孙肖肖,牟少敏,许永玉,等.基于深度学习的复杂背景下茶叶嫩芽检测算法[J]. 河北大学学报(自然科学版), 2019, 39(2):211-216.
[11]陈锋军,王成翰,顾梦梦,等.基于全卷积神经网络的云杉图像分割算法[J].农业机械学报,2018,49(12):188-194,210.
[12]孙俊,谭文军,武小红,等.多通道深度可分离卷积模型实时识别复杂背景下甜菜与杂草[J]. 农业工程学报,2019,35(12):184-190.
[13]张芳,王璐,付立思,等.复杂背景下黄瓜病害叶片的分割方法研究[J].浙江农业学报, 2014,26(5):1346-1355.
[14]宋熙煜,周利莉,李中国,等.图像分割中的超像素方法研究综述[J].中国图象图形学报, 2018, 20(5):599-608.
[15]ACHANTA R,SHAJI A,SMITH K,et al.SLIC superpixels compared to state-of-the-art superpixel methods[J].IEEE Transactions on Pattern and Machine Intelligence,2012,34(11):2274-2282.
[16]VLADIMIR V.Universal learning technology:Support vector machines[J].NEC Journal of Advanced Technology,2005,2(2):137-144.
[17]陈文颖,林永君,杨春来,等.基于SVM预测模型的光伏发电系统MPPT研究[J].太阳能学报,2013,34(2):245-250.
[18]YASSINE B,TAYLOR P, STORY A. Fully automated lung segmentation from chest radiographs using SLICO superpixels[J].Analog Integrated Circuits & Signal Processing,2018,95(3):423-428.
[19]LIU Y J,YU C C,YU M J,et al.Manifold SLIC:A fast method to compute content-Sensitive superpixels[C]//IEEE Computer Society. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco,CA,USA:IEEE Computer Society,2016:651-659.
[20]BUYSSENS P,GARDIN I,RUAN S,et al.Eikonal-based region growing for efficient clustering[J]. Image and Vision Computing, 2014, 32(12):1045-1054.

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备注/Memo

备注/Memo:
收稿日期：2019-12-23基金项目：安徽省高校自然科学研究重点项目(KJ2017A151);茶树生物学与资源利用国家重点实验室开放基金项目(SKLTOF20160202);安徽高校自然科学研究重大项目(KJ2019ZD20);国家重点研发计划项目(2016YFD0200900)作者简介：刘连忠(1968-)，男，安徽蚌埠人，博士，讲师，主要从事机器视觉、农业图像处理、智能农业研究。(E-mail)lzliu@ahau.edu.cn
更新日期/Last Update: 2020-09-08