[1]马立新,夏利利,刘璎瑛,等.基于图像处理的秧苗均匀度合格率检测[J].江苏农业学报,2022,38(02):387-393.[doi:doi:10.3969/j.issn.1000-4440.2022.02.012]
　MA Li-xin,XIA Li-li,LIU Ying-ying,et al.Seedling uniformity detection based on image processing[J].,2022,38(02):387-393.[doi:doi:10.3969/j.issn.1000-4440.2022.02.012]
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基于图像处理的秧苗均匀度合格率检测()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
38
期数:
2022年02期
页码:
387-393
栏目:
农业信息工程
出版日期:
2022-04-30

文章信息/Info

Title:
Seedling uniformity detection based on image processing
作者:
马立新1; 夏利利1; 刘璎瑛2; 李芃萱2; 朱伟3
(1.江苏省农业机械试验鉴定站，江苏南京210017；2.南京农业大学人工智能学院，江苏南京210031；3.南京农业大学工学院，江苏南京210031）
Author(s):
MA Li-xin1; XIA Li-li1; LIU Ying-ying2; LI Peng-xuan2; ZHU Wei3
(1.Jiangsu Agricultural Machinery Testing Station， Nanjing 210017，China；2.College of Artificial Intelligence，Nanjing Agricultural University， Nanjing 210031， China；3.College of Engineering， Nanjing Agricultural University， Nanjing 210031，China）
关键词:
秧苗茎部图像; 图像分割; 形态学操作; 均匀度检测
Keywords:
image of seedling stems; image segmentation; morphological operation; uniformity detection
分类号:
TP751;S511.01
DOI:
doi:10.3969/j.issn.1000-4440.2022.02.012
文献标志码:
A
摘要:
插秧机试验鉴定时要人工测定插秧前秧苗培育均匀度，为提高鉴定效率，本研究提出基于图像分割和形态学操作的秧苗均匀度合格率自动检测方法。首先将获取的秧苗根茎部图像在2G-R-B颜色空间进行灰度化处理，阈值分割后进行形态学操作，完成面积阈值和形状阈值的二次分割，得到只含有水稻秧苗的二值化图像；其次根据移距和秧苗深度确定取样方格大小，按方格大小进行图像划分，选取图像中间部分的20个小格，输出每小格内的秧苗数量，与农艺要求进行比对，符合要求记作该小格合格；最后根据DG/T 008-2019《农业机械推广鉴定大纲水稻插秧机》，计算3个不同苗盘图像的合格方格数，得到插秧前秧苗均匀度的合格率。结果表明，采用图像处理方法可以实现插秧前秧苗的均匀度合格率计算，秧苗统计的准确率可以达到97.95%，方格检测的准确度可以达到96.67%。处理每幅图像的平均耗时为2.461 s，大大提高了检测效率。
Abstract:
The cultivation uniformity of seedlings before transplanting needs to be measured manually in the identification process of seedling transplanter. To improve the identification efficiency, an automatic detection method for the pass rate of seedling uniformity was proposed based on image segmentation and morphological operation. Firstly, images of the seedling roots and stems were processed using graying method in 2G-R-B color space, and were processed by morphological operations after threshold segmentation by Otsu method. Binary images containing rice seedlings only were obtained after secondary division of the area threshold and shape threshold. Secondly, sizes of the sampling squares were determined by shift distance and seedling depth, then the images were divided into squares by the size. After selecting 20 small grids in the middle part of the images, the number of seedlings in each small grid was output and compared with agricultural requirements, and the confirmed small grid was recorded as qualified. Lastly, based on DG/T 008-2019 , number of the qualified squares from images of three different seedling plates were counted, and the pass rate of seedling uniformity before transplanting was calculated. The results showed that, the pass rate of seedling uniformity could be calculated before transplanting by image processing methods, the statistic accuracy of seedlings could reach 97.95%, and the accuracy of the grid detection could reach 96.67%. The average time for processing each image took 2.461 s, which improved the detection efficiency greatly.

参考文献/References:

[1]崔志英. 水稻插秧机研究现状及发展趋势[J].农业工程，2015，5(4):41-42.
[2]张树阁,苏春华,周磊,等. 促进水稻种植机械化水平提高[J].农机科技推广，2017(10):19-20.
[3]韩峰. 水稻机械化插秧技术分析与种植机械发展趋势[J].农业开发与装备， 2019(5):208-212.
[4]李春元. 对水稻插秧机主要项目测定与数据处理的探讨[J].农机论坛，2016(6):15-16.
[5]中华人民共和国国家质量监督检验检疫总局、中国国家标准化管理委员会.中华人民共和国国家标准水稻插秧机试验方法：GB/T 6243-2017[S].北京：中国标准出版社.
[6]廖娟,陈民慧,汪鹞,等. 基于双重 Gamma 校正的秧苗图像增强算法[J]．江苏农业学报，2020，36(6): 1411-1418．
[7]陈旭君,王承祥,朱德泉,等. 基于YOLO卷积神经网络的水稻秧苗行线检测[J].江苏农业学报,2020,36(4):930-935.
[8]陈信新. 基于机器视觉算法的水稻秧苗状态识别[J].计算机应用研究,2019,36(5):2-5.
[9]WOEBBECKE D M, MEYE G E, BARGEN K V, et al. Shape features of identifying yong weeds using image analysis[J].Transactions on American Society of Agricultural Engineering,1995,38(1):271-281.
[10]MEYE G E, HINDMAN T W, LAKSMI K. Machine vision detection parameters for plants species identification[C]. Bellingham, WA:SPIE,1999.
[11]NETO J C, MAYER G E. Crop species identification using machine vison of compute extracted individual leaves[J]. Proc Spie,2005，5996（11）：64-74.
[12]KATAOKA T, KANEKO T, OKAMOTO H, et al. Crop growth estimation system using machine vision[C]. Piscataway,NJ:IEEE,2003.
[13]龚立雄. 基于ComVI和双阈值OTSU算法的农作物图像识别[J]. 排灌机械工程学报，2014，32(4):364-368.
[14]ELAZIZ M A, OLIVAD, EWEES A A，et al. Multi-level thresholding based grey scale image segmentation using multi-objective multiverse optimizer[J]. Expert Systems with Applications,2019,125：1-37.
[15]袁加红，朱德泉，孙丙宇，等．基于机器视觉的水稻秧苗图像分割[J]. 浙江农业学报,2016,28(6):1069-1075．
[16]周俊,王明军,邵乔林. 农田图像绿色植物自适应分割方法[J]. 农业工程学报， 2013,29(18)：163-169.
[17]白元明，孔令成，张志华，等．基于改进 OTSU 算法的快速作物图像分割[J].江苏农业科学,2019,47(24):231-236．
[18]耿楠，于伟，宁纪锋．基于水平集和先验信息的农业图像分割方法[J].农业机械学报，2011,42(9):167-172．
[19]张志斌,罗锡文,臧英,等. 基于颜色特征的绿色作物图像分割算法[J]. 农业工程学报, 2011, 27(7):183-189.
[20]迟德霞,张伟,王洋. 基于EXG因子的水稻秧苗图像分割[J]. 安徽农业科学, 2012, 40(36):17902-17903.
[21]王雪,尹来武,郭鑫鑫. 室外多变光照条件下农田绿色作物的图像分割方法[J]. 吉林大学学报(理学版)，2018,56(5):1213-1218.
[22]OTSU N .A Threshold selection method from gray-level histograms[J]. IEEE Transaction System,Man and Cybenetics,1979, 9(1):62-66.
[23]中华人民共和国农业农村部：农业机械推广鉴定大纲-水稻插秧机：DG/T 008-2019[S]. 北京：中国农业出版社.

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备注/Memo

备注/Memo:
收稿日期：2021-08-10基金项目：江苏省现代农机装备与技术示范推广项目（NJ2019-25）作者简介：马立新（1966-），男，江苏溧阳人，研究员，主要从事农业机械试验鉴定工作。(E-mail)njmlxin@126.com通讯作者：刘璎瑛，（E-mail）lyy@njau.edu.cn
更新日期/Last Update: 2022-05-07