基于标尺图像识别的作物株高测量

doi:10.16180/j.cnki.issn1007-7820.2022.07.006

电子科技 ›› 2022, Vol. 35 ›› Issue (7): 32-39.doi: 10.16180/j.cnki.issn1007-7820.2022.07.006

基于标尺图像识别的作物株高测量

孙翔^1,⁴,裴晓芳^1,^2,³,周望⁴,朱平⁴

1. 南京信息工程大学电子与信息工程学院,江苏南京 210044
2. 南京信息工程大学滨江学院电子与信息工程学院,江苏无锡 214105
3. 南京信息工程大学江苏省大气环境与装备技术协同创新中心, 江苏南京 210044
4. 航天新气象科技有限公司,江苏无锡 214127

收稿日期:2021-01-17 出版日期:2022-07-15 发布日期:2022-08-16
作者简介:孙翔(1995-),男,硕士研究生。研究方向:图像处理、FPGA设计。|裴晓芳(1978-),女,副教授。研究方向:信号处理与应用。
基金资助:
国家自然科学基金(61601229);南京信息工程大学滨江学院项目(2019BJYNG006)

Crop Height Measurement Based on Ruler Image Recognition

SUN Xiang^1,⁴,PEI Xiaofang^1,^2,³,ZHOU Wang⁴,ZHU Ping⁴

1. School of Electronic & Information Engineering,Nanjing University of Information Science & Technology, Nanjing 210044,China
2. School of Electronic & Information Engineering,Binjiang College, Nanjing University of Information Science & Technology,Wuxi 214105,China
3. Collaborative Innovation Center of Atmospheric Environment and Equipment Technology of Jiangsu Province, Nanjing University of Information Science and Technology,Nanjing 210044,China
4. Aerospace Newsky Technology Co.,Ltd,Wuxi 214127,China

Received:2021-01-17 Online:2022-07-15 Published:2022-08-16
Supported by:
National Natural Science Foundation of China(61601229);Binjiang College, Nanjing University of Information Science & Technology Research Project(2019BJYNG006)

摘要/Abstract

摘要：

作物株高的测量是农作物自动观测中的重要环节,能直接反映作物的生长情况。针对人工测量作物株高成本较高且个人的主观影响较大的问题,文中提出了一种通过图像处理来测量株高的方法。将标尺置于作物主茎秆后方并拍摄标尺的照片,在MATLAB上将图像转换到HSV颜色空间分割标尺色块并进行滤波。采用连通域分析法计算标尺色块的像素高度,通过标尺像素高度和实际高度的比例关系计算株高。以灌木丛为实验对象拍摄照片并进行测量,然后对比实验测得的株高数据与人工测量数据,结果显示测量误差小于0.017 3 m,满足自动观测株高的标准。

关键词: 作物株高, 标尺, 图像处理, HSV, 颜色空间, 图像分割, 连通域, MATLAB

Abstract:

Crop height measurement is an important part of automatic crop observation, which can directly reflect the growth of crops. To solve the problem that the cost of artificial measurement of crop height is higher and the subjective influence of individual is greater, this study presents a method of measuring crop height by image processing. The ruler is placed behind the main stalk of the crop to take the photo of the ruler. The obtained images were converted to HSV color space on MATLAB to divide and filter the color blocks of the ruler. The pixel height of the color block is calculated by the connected domain analysis method. The plant height is calculated by the ratio relation between the ruler pixel height and the actual height. The shrubbery is taken as the experimental object to take photos and make measurement. Comparing the measured plant height data with manual measurement data, the results show that the measurement error is less than 0.0173 m, which meets the standard of automatic observation of plant height.

Key words: crop height, ruler, image processing, HSV, color space, image segmentation, connected domain, MATLAB

中图分类号:

TP302

孙翔,裴晓芳,周望,朱平. 基于标尺图像识别的作物株高测量[J]. 电子科技, 2022, 35(7): 32-39.

SUN Xiang,PEI Xiaofang,ZHOU Wang,ZHU Ping. Crop Height Measurement Based on Ruler Image Recognition[J]. Electronic Science and Technology, 2022, 35(7): 32-39.

图/表 10

图1

图2

图3

图4

图5

图6

图7

表1

表2

表3

参考文献 19

[1]	Chaudhury A, Ward C, Talasaz A, et al. Computer vision based autonmous robotic system for 3D plant growth measurement[C]. Halifax: The Twelfth Conference on Computer and Robot Vision, 2015.
[2]	Detweiler C, Anthony D, Elbaum S.Crop height estimation with unmanned aerial vehicles: U.S, US009969492B2[P]. 2018-05-15.
[3]	程曼, 蔡振江, Wang Ning, 等. 基于地面激光雷达的田间花生冠层高度测量系统研制[J]. 农业工程学报, 2019, 35(1):180-187.
	Cheng Man, Cai Zhenjiang, Wang Ning, et al. System design for peanut canopy height information acquisition based on LiDAR[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(1):180-187.
[4]	仇瑞承, 苗艳龙, 季宇寒, 等. 基于RGB-D相机的单株玉米株高测量方法[J]. 农业机械学报, 2017, 48(S1):211-219.
	Qiu Ruicheng, Miao Yanlong, Ji Yuhan, et al. Measurement of individual maize height based on RGB-D camera[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(S1):211-219.
[5]	杨斯, 高万林, 米家奇, 等. 基于RGB-D相机的蔬菜苗群体株高测量方法[J]. 农业机械学报, 2019, 50(S1):128-135.
	Yang Si, Gao Wanlin, Mi Jiaqi, et al. Method for measurement of vegetable seedlings height based on RGB-D camera[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(S1):128-135.
[6]	胡杨. 基于深度相机的叶菜类作物三维重建与生长测量方法研究[D]. 杭州: 浙江大学, 2018.
	Hu Yang. Study on 3D reconstruction and growth measurement of methods for leafy crops based on depth camera[D]. Hangzhou: Zhejiang University, 2018.
[7]	郝慧鹏, 田跃, 张南, 等. 基于HALCON的双目视觉株高测量[J]. 电子技术与软件工程, 2013,(22):108-109.
	Hao Huipeng, Tian Yue, Zhang Nan, et al. Binocular vision plant height measurement based on HALCON[J]. Electronic Technology and Software Engineering, 2013,(22):108-109.
[8]	张加楠. 基于视觉分析的作物自动观测关键技术研究[D]. 北京: 北京工业大学, 2017.
	Zhang Jianan. Research on key issues of automatic observation of crop based on visual analysis[D]. Beijing: Beijing University of Technology, 2017
[9]	尹世和, 梁玉, 韦鸿钰, 等. 基于双目立体视觉的植株生长状态无损监测方法的研究[J]. 现代农业装备, 2014(3):36-39.
	Yin Shihe, Liang Yu, Wei Hongyu, et al. The research of non-destructive measurement which based on the binocular stereoscopic vision[J]. Modern Agricultural Equipment, 2014(3):36-39.
[10]	孙晓峰, 姚笛, 刘书杰, 等. 海上钻井平台井喷液柱高度的图像识别测量方法[J]. 天然气工业, 2019, 39(9):96-101.
	Sun Xiaofeng, Yao Di, Liu Shujie, et al. A method for measuring the heights of blowout liquid columns on an offshore drilling platform based on image recognition[J]. Natural Gas Industry, 2019, 39(9):96-101.
[11]	杨云辉. 基于单目视觉的工件定位技术研究[J]. 电子科技, 2019, 32(12):72-75.
	Yang Yunhui. Research on workpiece location technology based on monocular vision[J]. Electronic Science and Technology, 2019, 32(12):72-75.
[12]	张国权, 李战明, 李向伟, 等. HSV空间中彩色图像分割研究[J]. 计算机工程与应用, 2010, 46(26):179-181.
	Zhang Guoquan, Li Zhanming, Li Xiangwei, et al. Research on color image segmentation in HSV space[J]. Computer Engineering and Applications, 2010, 46(26):179-181.
[13]	刘永波, 雷波, 胡亮, 等. 机器视觉在HSV颜色空间下稻瘟病病程分级判定研究[J]. 农学学报, 2020, 10(10):83-90.
	Liu Yongbo, Lei Bo, Hu Liang, et al. Study on classification of rice blast disease course based on machine vision in HSV color space[J]. Journal of Agriculture, 2020, 10(10):83-90.
[14]	张阵委, 章伟, 龙林, 等. 基于深度相机的小型无人机室内三维地图构建[J]. 电子科技, 2021, 34(1):65-70.
	Zhang Zhenwei, et al. Zhang Wei,long Lin,3D map construction of micro UAV based on depth camera[J]. Electronic Science and Technology, 2021, 34(1):65-70.
[15]	王雪晴, 郭亚航. 基于视觉技术的植物叶片面积测量研究[J]. 农业与技术, 2020, 40(20):8-11.
	Wang Xueqing, Guo Yahang. Measurement of plant leaf area based on visual technology[J]. Agriculture and Technology, 2020, 40(20):8-11.
[16]	孔斌. 快速连通域分析算法及其实现[J]. 模式识别与人工智能, 2003, 16(1):110-115.
	Kong Bin. A fast connected component analysis algorithm and its implementation[J]. Pattern Recognition and Artificial Intelligence, 2003, 16(1):110-115.
[17]	岳学军, 蔡雨霖, 王林惠, 等. 农情信息智能感知及解析的研究进展[J]. 华南农业大学学报, 2020, 41(6):14-28.
	Yue Xuejun, Cai Yulin, Wang linhui, et al. Research progress on intelligent perception and analytics of agricultural information[J]. Journal of South China Agricultural University, 2020, 41(6):14-28.
[18]	马旭东, 袁锐波. 基于图像检测的硬币面值识别方法[J]. 电子科技, 2020, 33(2):43-47.
	Ma Xudong, Yuan Ruibo. An identification method of coins denomination based on image detection[J]. Electronic Science and Technology, 2020, 33(2):43-47.
[19]	桂欣悦, 李振伟, 吴晨晨, 等. 基于MATLAB的红绿灯识别系统研究[J]. 电子设计工程, 2020, 28(16):133-136.
	Gui Xinyue, Li Zhenwei, Wu Chenchen, et al. Traffic light recognition system based on MATLAB[J]. Electronic Design Engineering, 2020, 28(16):133-136.

拍照时间	机位1	机位2	机位3	人工测量
9:00	0.601 7 m	0.595 2 m	0.593 1 m
14:00	0.595 5 m	0.601 3 m	0.593 8 m	0.610 4 m
17:00	0.602 4 m	0.594 5 m	0.597 4 m

拍照时间	灌木丛1	灌木丛2	灌木丛3
9:00	0.601 7 m	0.802 1 m	0.597 0 m
14:00	0.595 5 m	0.791 3 m	0.599 3 m
17:00	0.602 4 m	0.794 7 m	0.604 1 m
人工测量	0.610 4 m	0.805 4 m	0.608 6 m

植株高度	一级误差	二级误差	三级误差	四级误差
0.0~0.1	≤0.02	0.02~0.03	0.03~0.06	>0.06
0.1~0.2	≤0.03	0.03~0.04	0.04~0.07	>0.07
0.2~0.4	≤0.04	0.04~0.06	0.06~0.09	>0.09
0.4~1.0	≤0.05	0.05~0.10	0.10~0.14	>0.14
1.0~2.0	≤0.06	0.06~0.12	0.12~0.16	>0.16
>2.0	≤0.07	0.07~0.14	0.14~0.18	>0.18

基于标尺图像识别的作物株高测量

Crop Height Measurement Based on Ruler Image Recognition

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 19

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	冯俊逸,沈拓,张轩雄. 基于图像处理的轨旁信号机识别方法[J]. 电子科技, 2022, 35(8): 53-57.
[2]	张茂林,叶轻舟,潘鑫,陆华. 基于Tesseract_OCR的化工包装袋喷码质量检测算法[J]. 电子科技, 2022, 35(7): 27-31.
[3]	张崇崇,黄亚宇. GA-BP神经网络对片烟结构的预测研究[J]. 电子科技, 2022, 35(6): 35-42.
[4]	杨孙运,阚秀. 基于图像和IMU传感器的生物行为分析系统设计[J]. 电子科技, 2022, 35(4): 28-34.
[5]	纪玲玉,高永彬,赵呈陆,汤先华,徐凯成,徐嘉诚. 基于3D全卷积网络的腹部动脉CTA分割算法[J]. 电子科技, 2022, 35(3): 38-44.
[6]	齐美义,廖爱华. 基于MATLAB App Designer的牵引电机滚动轴承可靠性评估系统设计[J]. 电子科技, 2022, 35(3): 79-86.
[7]	刘建龙,郝正航. 基于背靠背变流器的风电系统仿真对比研究[J]. 电子科技, 2022, 35(2): 67-73.
[8]	刘晨旻,王亚刚. 基于连续空间的萤火虫算法改进[J]. 电子科技, 2022, 35(2): 40-45.
[9]	宗圣康,程建鹏,张西良. 基于光斑位置的起重机轨道高度差自动检测方法[J]. 电子科技, 2022, 35(1): 21-28.
[10]	牛帅臣,王福忠,韩耀飞,何国锋,牛叶克. 电网不平衡条件下模块化多电平变换器控制策略[J]. 电子科技, 2022, 35(1): 80-86.
[11]	王阳,王亚刚. 基于阶跃响应和遗传算法优化高阶加时滞模型的辨识方法[J]. 电子科技, 2021, 34(9): 41-46.
[12]	李玉东,连海山,胡晓丹. 三相输入型阻塞式交交变频电源[J]. 电子科技, 2021, 34(9): 47-53.
[13]	闫振彬,郑柏超,周志勇. 基于新型鲁棒下垂控制方法的光伏发电系统并网研究[J]. 电子科技, 2021, 34(8): 79-86.
[14]	司明明,陈玮,胡春燕,尹钟. 融合Resnet50和U-Net的眼底彩色血管图像分割[J]. 电子科技, 2021, 34(8): 19-24.
[15]	杨凌霄,王振营,刘群坡,王高伟,杨彦超. 基于H-S直方图的苹果着色率检测算法研究[J]. 电子科技, 2021, 34(5): 13-17.