Automatic extraction device and experiment of rice seed investigation parameters

XING Hang; HUANG Xu’nan; YANG Xiuli; WANG Zhiqi; QI Long

doi:10.7671/j.issn.1001-411X.202208022

Journal of South China Agricultural University > 2023 > 44(6): 968-977. > DOI: 10.7671/j.issn.1001-411X.202208022

XING Hang, HUANG Xu’nan, YANG Xiuli, et al. Automatic extraction device and experiment of rice seed investigation parameters[J]. Journal of South China Agricultural University, 2023, 44(6): 968-977. DOI: 10.7671/j.issn.1001-411X.202208022

Citation:

PDF (2116 KB)

Automatic extraction device and experiment of rice seed investigation parameters

College of Engineering, South China Agricultural University, Guangzhou 510642, China

More Information

Received Date: August 15, 2022
Available Online: November 12, 2023
Published Date: January 04, 2023

Graphical Abstract

Abstract

Abstract

Objective
The measurement accuracy and efficiency of traditional seed investigation methods could not meet the needs of modern rice breeding research. A synchronous collection device of rice grain image and quality information was designed to automatically extract rice seed investigation parameters.
Method
The image of grain region was automatically extracted by mask method, and the total number of rice grains was obtained according to the law between rice projection area and rice quantity. Empty grains were identified according to the difference of the hull contour between empty grains and full grains. Based on the mean value calibration method of corner spacing, the grain length and width were obtained by combining the minimum circumscribed rectangle method of contour, and the grain perimeter was obtained by combining the chain code method. The square area mean calibration method and pixel accumulation method were used to obtain the grain area. The effects of camera height, grain quantity, grain type and regular graph type on the extraction accuracy of grain character parameters were analyzed.
Result
The camera height had a obvious impact on the measurement accuracies of total number, empty number, length and width of rice grains, the grain type had a obvious impact on the measurement accuracy of width, and the regular graph type had a obvious impact on the measurement accuracies of grain area and perimeter. The determination coefficients (R²) of total grain number, empty grain number, grain length, grain width, grain perimeter and grain area measured by the proposed method were 0.99830, 0.98780, 0.99610, 0.78290, 0.99510 and 0.99998 respectively, the average measurement accuracies were 99.47%, 87.17%, 96.55%, 96.36%, 98.00% and 95.86% respectively, and the measurement efficiency was 16.52 grains per second.
Conclusion
The automatic extraction method of rice seed investigation parameters used in this paper is feasible, and can provide technical references for the development of automatic seed investigation machine.
- Rice,
- Seed investigation,
- Parameter extraction,
- Image processing

FullText(HTML)

References (33)

References

[1]	邓若玲, 潘威杰, 王志琪, 等. 农作物表型技术及其智能装备研究进展与展望[J]. 现代农业装备, 2021, 42(1): 2-9.
[2]	FURBANK R T, TESTER M. Phenomics-technologies to relieve the phenotyping bottleneck[J]. Trends in Plant Science, 2011, 16(12): 635-644. doi: 10.1016/j.tplants.2011.09.005
[3]	AFONNIKOV D A, GENAEV M A, DOROSHKOV A V, et al. Methods of high-throughput plant phenotyping for large-scale breeding and genetic experiments[J]. Russian Journal of Genetics, 2016, 52(7): 688-701. doi: 10.1134/S1022795416070024
[4]	周洪垒. 基于图像处理的水稻考种系统的设计与实现[D]. 成都: 电子科技大学, 2019.
[5]	FIORANI F, SCHURR U. Future scenarios for plant phenotyping[J]. Annual Review of Plant Biology, 2013, 64: 267-291. doi: 10.1146/annurev-arplant-050312-120137
[6]	DENG R L, TAO M, HUANG X N, et al. Automated counting grains on the rice panicle based on deep learning method[J]. Sensors, 2021, 21(1): 281. doi: doi.org/10.3390/s21010281.
[7]	VIRLET N, SABERMANESH K, SADEGHI-TEHRAN P, et al. Field scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring[J]. Functional Plant Biology, 2016, 44(1): 143-153.
[8]	GUO Q H, WU F F, PANG S X, et al. Crop 3D: A LiDAR based platform for 3D high-throughput crop phenotyping[J]. Science China-Life Sciences, 2018, 61(3): 328-339. doi: 10.1007/s11427-017-9056-0
[9]	宋鹏, 张晗, 王成, 等. 玉米籽粒考种信息获取装置设计与试验[J]. 农业机械学报, 2017, 48(12): 19-25. doi: 10.6041/j.issn.1000-1298.2017.12.002
[10]	胡正方, 向阳, 熊瑛, 等. 基于机器视觉的千粒质量测量仪的设计与试验[J]. 湖南农业大学学报(自然科学版), 2021, 47(4): 476-481. doi: 10.13331/j.cnki.jhau.2021.04.017
[11]	崔亮. 基于机器视觉的农作物种子计数检测系统[D]. 太原: 中北大学, 2016.
[12]	杨万能. 水稻产量相关性状参数自动提取的数字化技术研究[D]. 武汉: 华中科技大学, 2011.
[13]	宋初苏, 蓝景针, 夏世峰, 等. ZPXG-18型转盘斜刮式自动光电数粒仪和千粒重仪的设计[J]. 浙江农业学报, 2011, 23(5): 1023-1028. doi: 10.3969/j.issn.1004-1524.2011.05.030
[14]	ARVIDSSON S, PÉREZ-RODRÍGUEZ P, MUELLER-ROEBER B. A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects[J]. The New Phytologist, 2011, 191(3): 895-907. doi: 10.1111/j.1469-8137.2011.03756.x
[15]	YANG W N, GUO Z L, HUANG C L, et al. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice[J]. Nature Communications, 2014, 5(1): 1-9. doi: 10.1038/ncomms6087
[16]	段凌凤, 杨万能. 水稻表型组学研究概况和展望[J]. 生命科学, 2016, 28(10): 1129-1137. doi: 10.13376/j.cbls/2016151
[17]	HAIN R, KÄHLER C J, TROPEA C. Comparison of CCD, CMOS and intensified cameras[J]. Experiments in Fluids, 2007, 42(3): 403-411. doi: 10.1007/s00348-006-0247-1
[18]	袁洪波, 赵努东, 程曼. 基于图像处理的田间杂草识别研究进展与展望[J]. 农业机械学报, 2020, 51(S2): 323-334. doi: 10.6041/j.issn.1000-1298.2020.S2.038
[19]	夏庆凯, 于大泳. 基于图像处理的工业生产手机屏幕裂纹检测[J]. 农业装备与车辆工程, 2021, 59(1): 112-115. doi: 10.3969/j.issn.1673-3142.2021.01.024
[20]	项荣, 应义斌, 蒋焕煜. 田间环境下果蔬采摘快速识别与定位方法研究进展[J]. 农业机械学报, 2013, 44(11): 208-223. doi: 10.6041/j.issn.1000-1298.2013.11.037
[21]	孔明, 徐志玲, 徐勇, 等. 实际边缘轮廓下的螺栓综合尺寸质量检测[J]. 计量学报, 2021, 42(6): 724-730. doi: 10.3969/j.issn.1000-1158.2021.06.06
[22]	贾建楠, 吉海彦. 基于病斑形状和神经网络的黄瓜病害识别[J]. 农业工程学报, 2013, 29(S1): 115-121.
[23]	李擎, 唐欢, 迟健男, 等. 基于改进最大类间方差法的手势分割方法研究[J]. 自动化学报, 2017, 43(4): 528-537. doi: 10.16383/j.aas.2017.c150862
[24]	伍艳莲, 赵力, 姜海燕, 等. 基于改进均值漂移算法的绿色作物图像分割方法[J]. 农业工程学报, 2014, 30(24): 161-167. doi: 10.3969/j.issn.1002-6819.2014.24.019
[25]	胡炼, 刘海龙, 何杰, 等. 智能除草机器人研究现状与展望[J]. 华南农业大学学报, 2023, 44(1): 34-42. doi: 10.7671/j.issn.1001-411X.202203060
[26]	贺锋, 钟宏民, 胡友旺. 基于图像处理的焊缝跟踪检测方法研究[J]. 应用激光, 2020, 40(5): 847-854. doi: 10.14128/j.cnki.al.20204005.847
[27]	胡园园. 施肥与灌溉对水稻颖壳不闭合影响研究[D]. 合肥: 安徽农业大学, 2011.
[28]	赵凯旋, 刘晓航, 姬江涛. 基于EfficientNet与点云凸包特征的奶牛体况自动评分[J]. 农业机械学报, 2021, 52(5): 192-201. doi: 10.6041/j.issn.1000-1298.2021.05.021
[29]	赵强, 黄远, 赵春海. 基于凸包的电池片边缘缺陷检测算法[J]. 计算机测量与控制, 2020, 28(6): 129-134. doi: 10.16526/j.cnki.11-4762/tp.2020.06.027
[30]	李鹏阳, 杨文辉, 曹利平, 等. 圆盘类零件几何量机器视觉测量技术研究[J]. 西安工业大学学报, 2020, 40(6): 598-604. doi: 10.16185/j.jxatu.edu.cn.2020.06.002
[31]	张文霞, 王春光, 王海超, 等. 无芒隐子草叶片卷曲度和厚度测量方法[J]. 农业机械学报, 2021, 52(5): 184-191. doi: 10.6041/j.issn.1000-1298.2021.05.020
[32]	古亭. 基于机器视觉的蚕茧计数与识别研究[D]. 杭州: 中国计量大学, 2020.
[33]	汪晓艳, 丁义明. 一种去除图像中Cauchy噪声的滤波算法[J]. 数学物理学报, 2018, 38(4): 823-832. doi: 10.3969/j.issn.1003-3998.2018.04.018

Cited By

Cited by

Periodical cited type(3)

1.	杨洲，区湘颖，张志宏，罗宇康，何俊宇，欧俊辰，段洁利. 早期香蕉枯萎病Foc4双探针核酸纸基检测传感器研制. 农业工程学报. 2024(05): 38-46 .
2.	杨亚娥，卢加举，王忠泽，刘凡值，罗立娜. 香蕉主要病虫害及防治措施研究进展. 现代农业科技. 2024(17): 86-93 .
3.	梁基校，陈东，利齐欣，林鸿生，张景欣，杨祁云，王飞钊，蒲小明. 香蕉种苗中枯萎病菌和细菌性软腐病菌实时荧光PCR检测. 农业研究与应用. 2023(05): 8-14 .

Other cited types(0)

Get Citation

PDF

XML

Article views (1) PDF downloads (0) Cited by(3)

Turn off MathJax

Article Contents

Abstract

References

Automatic extraction device and experiment of rice seed investigation parameters

Abstract

References

Cited by

Periodical cited type(3)

Other cited types(0)

Catalog

Export File

Citation

Format

Content