基于多重分形去趋势移动平均分析的油菜叶片氮素营养诊断建模

doi:10.11924/j.issn.1000-6850.casb15120055

中国农学通报 ›› 2016, Vol. 32 ›› Issue (12): 65-70.doi: 10.11924/j.issn.1000-6850.casb15120055

所属专题：油料作物；园艺

基于多重分形去趋势移动平均分析的油菜叶片氮素营养诊断建模

苏乐,邹锐标,王访

(湖南农业大学理学院,长沙 410128）

收稿日期:2015-12-09 修回日期:2016-02-21 接受日期:2016-02-25 出版日期:2016-04-27 发布日期:2016-04-27
通讯作者: 邹锐标
基金资助:
国家自然科学基金“基于角果多重分形的油菜氮素营养诊断建模”(31501227)；湖南省教育厅重点项目“基于多重分形理论的油菜氮素营养诊断研究”(15A083)；优秀青年项目“作物诊断的叶片图像多重分形方法与建模”(14B087)；湖南省重点研发计划项目“基于角果图像多重分形特征的油菜氮素营养诊断建模”(2015JC3098)。

Modeling for Rapeseed’s Leaf Nitrogen Nutrient Diagnosis Based on Multifractal Detrended Moving Average Analysis

Su Le, Zou Ruibiao, Wang Fang

(College of Science, Hunan Agricultural University, Changsha 410128)

Received:2015-12-09 Revised:2016-02-21 Accepted:2016-02-25 Online:2016-04-27 Published:2016-04-27

摘要/Abstract

摘要： 为识别和诊断不同施氮水平下油菜叶片纹理特征，利用多重分形去趋势移动平均分析(MF-DMA)，分别计算θ=0，θ=0.5，θ=1下油菜叶片图像的11个全局广义Hurst指数和其他6种相关的多重分形特征参数。利用不同的特征参数组合分别对基部叶片、中部叶片和顶部叶片进行氮素营养诊断识别。结果表明θ=0时诊断效果优于θ=0.5和θ=1；并且在θ=0时，基部叶片和中部叶片诊断效果优于顶部叶片，表明基部和中部叶片对氮素敏感性强于顶部。对3个部位混合的油菜样本进行施氮适中和亏缺两类定性诊断，结果表明支持向量机和随机森林这2种分类方法最佳，最佳识别准确率分别为95.81%和96.63%。说明该模型具有良好的有效性。

关键词: 豇豆, 豇豆, 遗传多样性, 亲缘关系, SRAP, SSR

Abstract: In order to identify and diagnose the texture features of rape leaf under different nitrogen levels, eleven kinds of generalized Hurst indexes and other six kinds of related multifractal characteristic parameters of the rape leaf images were calculated by using multifractal detrended moving average analysis (MF-DMA) with three key position parameters, namely, θ=0, 0.5 and 1, respectively. By applying different combinations of characteristic parameters, the nitrogen nutrition diagnosis and recognition were conducted for the base leaf, central leaf and top leaf, respectively. The results showed that the performance of diagnosis with position parameter of θ=0 was better than that with 0.5 and 1. In addition, the best diagnose accuracy came from the base leaf and the central leaf, which demonstrated that the base leaf and the central leaf were more sensitive to the nitrogen deficiency than the top leaf. By diagnosing the nitrogen deficiency and nitrogen moderation of the three parts of the mixed rape leaf samples, it showed that support vector machines and kernel method (SVMKM) and the random forest were the best two methods to obtain the accuracy, by which the best recognition accuracy rate reached 95.81% and 96.63%, respectively. It indicated that our model possessed good effectiveness.

苏乐,邹锐标,王访. 基于多重分形去趋势移动平均分析的油菜叶片氮素营养诊断建模[J]. 中国农学通报, 2016, 32(12): 65-70.

Su Le,Zou Ruibiao and Wang Fang. Modeling for Rapeseed’s Leaf Nitrogen Nutrient Diagnosis Based on Multifractal Detrended Moving Average Analysis[J]. Chinese Agricultural Science Bulletin, 2016, 32(12): 65-70.

参考文献

[1] Jua X T, Xing G X, Chen X P, et al. Reducing environmental risk by improving N management in intensive Chinese agricultural systems[J]. PNAS, 2009,106(9):3041-3046.
[2] 王勃,徐静.计算机视觉技术在苹果叶片营养诊断上的应用[J].农机化研究, 2008(3):238-239.
[3] 袁道军,刘安国,刘志雄,等.利用计算机视觉技术进行作物生长监测的研究进展[J].农业网络信息,2007(2):21-25.
[4] 刘洪见,郑丽敏,廖树华,等.计算机视觉技术在农作物氮素营养诊断上的应用研究进展[J].麦类作物学报,2005,25(5):117-121.
[5] 李锦卫,廖桂平.作物图像光照亮度补偿方法[J].农机化研究,2012(8):26-30.
[6] 李锦卫,廖桂平,金晶,等.基于灰度截留分割与十色模型的马铃薯表面缺陷检测方法[J].农业工程学报,2010,26(10):236-242.
[7] 王访,廖桂平,王晓乔,等.基于多重分形理论的油菜缺素叶片特征提取[J].农业工程学报,2013,29(24):181-189.
[8] Wendt H, Roux S, Jaffard S, et al. Wavelet leaders and bootstrap for multifractal analysis of images[J].Signal Process,2009,6(89):1100-1114.
[9] Abry P, Wendt H, Jaffard S. When Van Gogh meets Mandelbrot: multifractal classication of painting’s texture[J].Signal Process,2013,93(3):554-572.
[10] Stojic T, Reljin I, Reljin B. Adaptation of multifractal analysis to segmentation of micro-calcifications in digital mammograms[J].Physica A, 2006,367(15):494-508.
[11] Kantelhardt J W, Zschiegner S A, Bunde E K, et al. Multifractal detrended fluctuation analysis of non- stationary time series[J].Physica A: Statistical Mechanics and its Applications,2002(316):87-114.
[12] Gu G F, Zhou W X. Detrended fluctuation analysis for fractals and multifractals in higher dimensions[J].Physical Review E,74,061104,2006.
[13] Wang F, Liao G P, Li J W, et al. Multifractal detrended fluctuation analysis for clustering structures of electricity price periods[J].Physica A: Statistical Mechanics and its Applications,2013,392(22):5723-5734.
[14] Zhou Y, Leung Y. Multifractal temporally weighted detrended fluctuation analysis and its application in the analysis of scaling behavior in temperature series[J].Journal of Statistical Mechanics: Theory and Experiment, 2010, 2010(2010):361-394.
[15] Wang F, Li Z S, Liao G P. Multifractal Detrended Fluctuation Analysis for Image Texture Feature Representation[J].International Journal of Pattern Recognition and Artificial Intelligence,2014,28(3):145-505.
[16] Wang F, Li Z S, Li J W. Local Multifractal Detrended Fluctuation Analysis for Non-stationary Image''s Texture Segmentation[J].Applied Surface Science,2014,233:116-125.
[17] Wang F, Li J W, Shi W, et al. Leaf image segmentation method based on multifractal detrended fluctuation analysis[J].Journal of Applied Physics, 2013, 114(21):214905-214905-9.
[18] Alessio E, Carbone A, Castelli G, et al. Second-order moving average and scaling of stochastic time series[J].The European Physical Journal B-Condensed Matter and Complex Systems,2002,27(2):197-200.
[19] Gu G F, Zhou W X. Detrending moving average algorithm for multifractals[J].Physical Review E, 2010, 82(1 Pt 1):1859-1860.
[20] Schumann A Y, Kantelhardt J W. Multifractal moving average analysis and test of multifractal model with tuned correlations[J].Physica A: Statistical Mechanics and its Applications,2011,390(14):2637-2654.
[21] Wang Y, Wu C, Pan Z. Multifractal detrending moving average analysis on the US Dollar exchange rates[J].Physica A: Statistical Mechanics and its Applications,2011,390(20):3512-3523.
[22] Wang F, Wang L, Zou R B. Multifractal detrended moving average analysis for texture representation[J].Chaos: An Interdisciplinary Journal of Nonlinear Science, 2014, 24(3):5880-5885.
[23] Shi W, Zou R B, Wang F, et al. A new image segmentation method based on multifractal detrended moving average analysis[J].Physica A, Volume 432, 2015,432:197-205.
[24] Duda R O, Hart P E, Stork D G. Pattern classi?cation[M].2nd.New York:John Wiley & Sons,2001.
[25] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications[J].Neuro-computing,2006,12(1):489-501.
[26] Vladimir N. Vapnik. 统计学习理论[M].北京:电子工业出版社,2009.
[27] Nello Cristianini, JohnShawe, Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods[M].北京:电子工业出版社,2004.
[28] Ho, Kam T. Random Decision Forest. Proc. of the 3rd Int''l Conf. on Document Analysis and Recognition[C].Montreal,Canada,August 14-18,1995:278-282.
[29] Bremner D, Demaine E, Erickson J, et al. Output-sensitive algorithms for computing nearest-neighbor decision boundaries[J].Discrete and Computational Geometry,2005,33(4):593-604.
[30] 李锦卫. 基于计算机视觉的水稻、油菜叶色-氮营养诊断机理与建模[D].长沙:湖南农业大学,2010,12.
[31] Hastie T, Tibshirani R, Friedman J等译. 统计学习基础-数据挖掘[M].北京:电子工业出版社, 2007.

基于多重分形去趋势移动平均分析的油菜叶片氮素营养诊断建模

Modeling for Rapeseed’s Leaf Nitrogen Nutrient Diagnosis Based on Multifractal Detrended Moving Average Analysis

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

关于本刊

作者中心

审者中心

在线期刊

联系我们

[1]	李锐, 尚霄, 尚春树, 常利芳, 闫蕾, 白建荣. SSR荧光检测解析224份山西玉米自交系的遗传结构与遗传关系[J]. 中国农学通报, 2022, 38(5): 9-16.
[2]	刘小英, 吴碧君, 张游南, 黄飞龙, 刘国强. 基于ISSR标记的龙眼种质资源遗传多样性及亲缘关系分析[J]. 中国农学通报, 2022, 38(31): 60-65.
[3]	赵雅儒, 邳植, 刘蕊, 马语嫣, 吴则东. 不同甜菜单胚细胞质雄性不育系与保持系的遗传多样性分析[J]. 中国农学通报, 2022, 38(30): 35-40.
[4]	孙志广, 潘根, 陈庭木, 李景芳, 赵利君, 迟铭, 徐波, 邢运高, 刘金波, 刘晓敏, 葛高宁, 徐锦涛, 王宝祥, 徐大勇. 基于SNP标记的穞稻与栽培稻的遗传多样性分析及萌发耐淹性评价[J]. 中国农学通报, 2022, 38(30): 6-13.
[5]	宋垚, 王辉. 基于DNA序列的‘和田’玫瑰遗传背景分析研究[J]. 中国农学通报, 2022, 38(28): 36-40.
[6]	张佳琦, 郭宗珊, 刘长华, 李荣田. 黑龙江省水稻品种的遗传多样性[J]. 中国农学通报, 2022, 38(17): 1-9.
[7]	梁燕, 韩传明, 周继磊, 孙超, 王翠香, 李春明, 王静, 闵旭峰, 公庆党, 孟晓烨, 杨绪强. 山东核桃良种SSR指纹图谱及分子身份证的构建——基于毛细管电泳分析[J]. 中国农学通报, 2022, 38(15): 113-121.
[8]	彭婵, 张新叶, 刘宗坤, 马林江, 陈慧玲. 石斛属植物SSR分子标记的研究进展[J]. 中国农学通报, 2022, 38(13): 36-40.
[9]	汪乔, 王祥锋, 郭玉峰, 王丽. 肺形侧耳菌株的遗传差异分析与农艺性状评价[J]. 中国农学通报, 2022, 38(10): 46-52.
[10]	王洪秀, 孙鹏, 安颖, 胡佳, 陈绪涛, 李菁, 魏云辉. 18个茶树菇菌株的亲缘关系分析及农艺性状评价[J]. 中国农学通报, 2022, 38(1): 23-31.
[11]	向小华, 李媛, 张兴伟, 刘国祥, 杨菁, 赵文涛, 闻刚, 邵雨, 范静苑, 吕洪坤. 海南新收集烟草种质资源的鉴定与遗传多样性分析[J]. 中国农学通报, 2021, 37(7): 59-67.
[12]	王晓燕, 单红丽, 张荣跃, 仓晓燕, 王长秘, 李文凤, 尹炯, 罗志明, 黄应昆. 甘蔗抗褐锈病新基因抗感病池构建及SSR多态性引物筛选[J]. 中国农学通报, 2021, 37(6): 97-103.
[13]	张桂宁, 方弟安, 薛向平, 张敏莹, 冯晓婷, 杨雪军. 基于细胞色素B(Cyt b)基因对翘嘴鲌不同群体遗传多样性的分析[J]. 中国农学通报, 2021, 37(23): 118-124.
[14]	刘小英, 黄飞龙, 刘国强, 张游南. 枇杷ISSR分子标记的研究进展[J]. 中国农学通报, 2021, 37(21): 106-110.
[15]	徐明, 汪琼, 李冬玲, 郑玉红, 徐增莱. 基于ISSR标记的5个蓼蓝居群遗传多样性分析[J]. 中国农学通报, 2021, 37(21): 134-139.