[1]闫靖昆,黄毓贤,秦伟森,等.棉田复杂背景下棉花黄萎病病斑分割算法研究[J].南京师大学报(自然科学版),2021,44(04):127-134.[doi:10.3969/j.issn.1001-4616.2021.04.017]
　Yan Jingkun,Huang Yuxian,Qin Weisen,et al.Study on Segmentation Algorithm of Cotton Verticillium WiltDisease Spot in Cotton Field Under Complex Background[J].Journal of Nanjing Normal University(Natural Science Edition),2021,44(04):127-134.[doi:10.3969/j.issn.1001-4616.2021.04.017]
点击复制

棉田复杂背景下棉花黄萎病病斑分割算法研究()
分享到：

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第44卷
期数:
2021年04期
页码:
127-134
栏目:
·计算机科学与技术·
出版日期:
2021-12-15

文章信息/Info

Title:
Study on Segmentation Algorithm of Cotton Verticillium WiltDisease Spot in Cotton Field Under Complex Background
文章编号:
1001-4616(2021)04-0127-08
作者:
闫靖昆; 黄毓贤; 秦伟森; 高 攀
石河子大学信息科学与技术学院,新疆 石河子 832003
Author(s):
Yan Jingkun; Huang Yuxian; Qin Weisen; Gao Pan
College of Information Science and Technology,Shihezi University,Shihezi 832003,China
关键词:
机器视觉; 深度学习; 语义分割; 棉花病害; 大田环境
Keywords:
machine vision; deep learning; semantic segmentation; cotton disease; field environment
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2021.04.017
文献标志码:
A
摘要:
在利用机器视觉技术进行大田环境下棉花黄萎病的病叶分割与病斑提取过程中,由于光线明暗、与棉花叶片像素相近的杂草的影响,会出现过分割、误分割等情况. 针对此问题,本文提出了基于数据迁移的 DeepLabv3+模型与质心选择K均值聚类机制相结合的两阶段分割算法. 首先,利用基于数据增强的DeepLabv3+分割模型在复杂背景中提取到病叶; 然后在叶片HSV颜色空间中选取初始质心,利用K均值聚类算法得到病斑簇; 最后利用数据迁移的方法,把从源领域(Kaggle NPDD数据集)学习到的知识迁移到目标领域(棉花病叶),有效缓解了因为样本数据集数据量较少带来的过分割、误分割问题. 试验结果表明,棉花病叶的分割综合评价指标值为 98.87%,黄萎病病斑的分割综合评价指标值为87.29%. 本文提出的病斑分割算法能够有效分割复杂背景图像中出现的棉花病叶、病斑,时效性更强、准确度更高,可为后续棉花病害叶部图像的进一步识别处理提供技术支撑,为农作物病虫害识别技术的发展提供了算法参考.
Abstract:
In the process of cotton verticillium wilt segmentation and spot extraction using machine vision technology in the field environment,there will be over-segmentation and missegmentation due to the influence of the light and shade of weeds with similar pixels of cotton leaves. In order to solve the problems,a two-stage segmentation algorithm combining the DeepLabv3+ model based on transfer learning and the K-means clustering mechanism of centroid selection was proposed in this paper. First,the infected leaves were extracted from the complex background by the improved DeepLabv3+ segmentation model based on deep enhancement. Then,the initial centroid was selected from the HSV color space of the leaves,and the disinfected patches were obtained by the K-means clustering algorithm. Last,the deep transfer learning technology can be used to transfer the knowledge learned from the source domain(Kaggle NPDD data set)to the target domain(cotton infected leaves),and the problems of over-segmentation and missegmentation were effectively alleviated. The results showed that the segmentation index value of cotton infected leaves was 98.87%,and that of verticillium wilt spot was 87.29%. The algorithm proposed in this paper can effectively segment infected cotton leaves and disinfected cotton patches in complex background images,with stronger timing and higher accuracy. It can provide technical support for further recognition and processing of cotton disinfected leaves images in the future,and provide an algorithm reference for the development of crop pest identification technology.

参考文献/References:

[1] 朱荷琴,李志芳,冯自力,等. 我国棉花黄萎病研究十年回顾及展望[J]. 棉花学报,2017,29(S1):37-50.
[2]YANG F G,SHAN Z Y,KRUGGEL F. White matter lesion segmentation based on feature joint occurrence probability and χ2 random field theory from magnetic resonance(MR)images[J]. Pattern recognition letters,2010,31(9):781-790.
[3]袁媛,李淼,陈晟,等. 复杂背景黄瓜叶部病害图像分割方法[J]. 农业机械学报,2013,44(10):233-237.
[4]MOHANTY S P,HUGHES D P,SALATHé M. Using deep learning for image-based plant disease detection[J]. Frontiers in plant science,2016,7:1419-1429.
[5]任守纲,贾馥玮,顾兴健,等. 反卷积引导的番茄叶部病害识别及病斑分割模型[J]. 农业工程学报,2020,36(12):186-195.
[6]RONNEBERGER O,FISCHER P,BROX T. U-net:convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention,Munich,Germany,2015. Cham:Springer,2015:234-241.
[7]OZGUVEN M M,ADEM K. Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms[J]. Physica A:statistical mechanics and its applications,2019,535:122537.
[8]SHAKEEL P M,BURHANUDDIN M A,DESA M I. Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks[J]. Measurement,2019,145:702-712.
[9]龚勋,杨菲,杜章锦,等. 甲状腺、乳腺超声影像自动分析技术综述[J]. 软件学报,2020,31(7):2245-2282.
[10]LIU J. Feature recognition method for similar key points of human face based on adaptive median filter[J]. International journal of biometrics,2021,13(1):1-16.
[11]ZHUANG Y,LIU Q,QIU C J,et al. A handwritten Chinese character recognition based on convolutional neural network and median filtering[J]. Journal of physics conference series,2021,1820(1):012162.
[12]CHEN L C,ZHU Y,PAPANDREOU G,et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the European Conference on Computer Vision,Munich,Germany,2018. Cham:Springer,2018:801-818.
[13]刘广瑞,黄真,毛树春,等. 基于最小阈值法的棉花幼苗识别研究[J]. 棉花学报,2012,24(6):562-565.
[14]PRAJAPATI H B,SHAH J P,DABHI V K. Detection and classification of rice plant diseases[J]. Intelligent decision techno-logies,2017,11(3):357-373.
[15]吕峰,柴变芳,李文斌,等. 一种主动半监督K-means聚类算法的改进策略[J]. 南京师范大学学报(工程技术版),2018,18(2):56-62.

相似文献/References:

[1]郑德鹏,杜吉祥,翟传敏.基于深度学习MPCANet的年龄估计[J].南京师大学报(自然科学版),2017,40(01):20.[doi:10.3969/j.issn.1001-4616.2017.01.004]
　Zheng Depeng,Du Jixiang,Zhai Chuanmin.Age Estimation Based on Deep Learning MPCANet[J].Journal of Nanjing Normal University(Natural Science Edition),2017,40(04):20.[doi:10.3969/j.issn.1001-4616.2017.01.004]
[2]朱 繁,王洪元,张 继.基于深度学习的行人重识别研究综述[J].南京师大学报(自然科学版),2018,41(04):93.[doi:10.3969/j.issn.1001-4616.2018.04.015]
　Zhu Fan,Wang Hongyuan,Zhang Ji.A Survey of Person Re-identification Based on Deep Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2018,41(04):93.[doi:10.3969/j.issn.1001-4616.2018.04.015]
[3]孙茹君,张鲁飞.基于动态指导的深度学习模型稀疏化执行方法[J].南京师大学报(自然科学版),2019,42(03):11.[doi:10.3969/j.issn.1001-4616.2019.03.002]
　Sun Rujun,Zhang Lufei.Dynamic Sparse Method for Deep Learning Execution[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(04):11.[doi:10.3969/j.issn.1001-4616.2019.03.002]
[4]赵文芳,林润生,唐 伟,等.基于深度学习的PM2.5短期预测模型[J].南京师大学报(自然科学版),2019,42(03):32.[doi:10.3969/j.issn.1001-4616.2019.03.005]
　Zhao Wenfang,Lin Runsheng,Tang Wei,et al.Forecasting Model of Short-Term PM2.5 ConcentrationBased on Deep Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(04):32.[doi:10.3969/j.issn.1001-4616.2019.03.005]
[5]张新峰,闫昆鹏,赵 珣.基于双向LSTM的手写文字识别技术研究[J].南京师大学报(自然科学版),2019,42(03):58.[doi:10.3969/j.issn.1001-4616.2019.03.008]
　Zhang Xinfeng,Yan Kunpeng,Zhao Xun.Handwriting Chinese Text Recognition Using BiLSTM Network[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(04):58.[doi:10.3969/j.issn.1001-4616.2019.03.008]
[6]贾玉福,胡胜红,刘文平,等.使用条件生成对抗网络的自然图像增强方法[J].南京师大学报(自然科学版),2019,42(03):88.[doi:10.3969/j.issn.1001-4616.2019.03.012]
　Jia Yufu,Hu Shenghong,Liu Wenping,et al.Wild Image Enhancement with Conditional Generative Adversarial Network[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(04):88.[doi:10.3969/j.issn.1001-4616.2019.03.012]
[7]汤 凯,何 庆,赵 群,等.基于改进的深度残差网络的图像识别[J].南京师大学报(自然科学版),2019,42(03):115.[doi:10.3969/j.issn.1001-4616.2019.03.015]
　Tang Kai,He Qing,Zhao Qun,et al.Image Recognition Based on Improved Deep Neural Network[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(04):115.[doi:10.3969/j.issn.1001-4616.2019.03.015]
[8]汪 晨,张辉辉,乐继旺,等.基于深度学习和遥感影像的松材线虫病疫松树目标检测[J].南京师大学报(自然科学版),2021,44(03):84.[doi:10.3969/j.issn.1001-4616.2021.03.013]
　Wang Chen,Zhang Huihui,Le Jiwang,et al.Object Detection to the Pine Trees Affected by Pine Wilt Diseasein Remote Sensing Images Using Deep Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2021,44(04):84.[doi:10.3969/j.issn.1001-4616.2021.03.013]
[9]韩 悦,张永寿,郭依廷,等.乳腺癌腋窝淋巴结超声图像分割算法研究[J].南京师大学报(自然科学版),2021,44(04):122.[doi:10.3969/j.issn.1001-4616.2021.04.016]
　Han Yue,Zhang Yongshou,Guo Yiting,et al.Research on Ultrasound Image Segmentation Algorithm forAxillary Lymph Node with Breast Cancer[J].Journal of Nanjing Normal University(Natural Science Edition),2021,44(04):122.[doi:10.3969/j.issn.1001-4616.2021.04.016]
[10]朱丹浩,王 震,黄肖宇,等.基于深度学习的长语音口音识别研究[J].南京师大学报(自然科学版),2022,45(04):110.[doi:10.3969/j.issn.1001-4616.2022.04.015]
　Zhu Danhao,Wang Zhen,Huang Xiaoyu,et al.Research on Long Speech Accent Recognition Based on Deep Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2022,45(04):110.[doi:10.3969/j.issn.1001-4616.2022.04.015]

备注/Memo

备注/Memo:
收稿日期:2021-05-20.
基金项目:国家自然科学基金项目(61965014)、兵团“强青”科技创新骨干人才计划项目(2021CB030).
通讯作者:高攀,博士,教授,博士生导师,研究方向:多源数据融合智能检测、大数据、区块链技术. E-mail:gp_inf@shzu.edu.cn
更新日期/Last Update: 2021-12-15