Scientia Agricultura Sinica ›› 2020, Vol. 53 ›› Issue (8): 1545-1555.doi: 10.3864/j.issn.0578-1752.2020.08.005

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Weed Identification in Maize Field Based on Multi-Spectral Remote Sensing of Unmanned Aerial Vehicle

ZHAO Jing1,2,LI ZhiMing1,2,LU LiQun2,3,JIA Peng1,2,YANG HuanBo1,2,LAN YuBin1,2()   

  1. 1 School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, Shandong
    2 International Research Center of Precision Agriculture Aviation Application Technology, Shandong University of Technology, Zibo 255000, Shandong
    3 School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, Shandong
  • Received:2019-08-29 Accepted:2019-11-04 Online:2020-04-16 Published:2020-04-29
  • Contact: YuBin LAN E-mail:ylan@sdut.edu.cn

RichHTML

60

PDF

534

Abstract:

【Objective】In order to reduce the application rate of herbicides and to make the maize planting management more effective, the accurate identification of weeds in maize fields was investigated based on multi-spectral remote sensing of unmanned aerial vehicles (UAV). 【Method】In this paper, a Red Edge-M multi-spectral camera was mounted in a six-rotor UAV to acquire five single-band images of blue, green, red, red edge, and near-infrared, and the application was taken in Zibo, Shandong province, China to acquire multi-spectral images of a maize field in July 14, 2018. In order to separate the vegetation and non-vegetation pixels in the image, 7 vegetation indices were calculated, the OTSU method was used to obtain the non-vegetation area, and the multi-spectral image was masked. Then multi-spectral image was transformed by principal component analysis, retaining the first three principal component bands with the most information. The experimental region was divided into 3 training areas and 1 verification area. 675 maize and 525 weed samples were selected in the training areas to train the supervised classification model, and 240 maize and 160 weed samples were selected in the verification area to evaluate model classification accuracy. The 7 vegetation indices, 24 texture features of the 3 principal component bands and 10 reflectivity of multi-spectral image bands which were filtered, and a total of 41 features were taken as features of maize and weed. Support vector machines-feature recursive elimination (SVM-RFE) algorithm and Relief algorithm were applied to selecting 14 features from 41 features to constitutes a feature subset separately, and supervised classification for weed detection was performed using support vector machine (SVM), K-nearest neighbor (KNN), Cart decision tree (Cart), random forest (RF) and artificial neural network (ANN) .【Result】SVM and RF performed a better classification with all features and SVM-RFE & Relief feature subsets. The overall accuracy of SVM was 89.13%-91.94%, Kappa>0.79, and overall accuracy of random forest was 89.27%-90.95%, Kappa>0.79.【Conclusion】 SVM-RFE feature selection algorithm was better than the Relief feature algorithm for reducing the original features. SVM model had the highest classification accuracy for identification of weed and maize at regional canopy scales.

Key words: weed identification, UAV remote sensing, multi-spectral image, feature selection, supervised classification

Fig. 1

Experimental region"

Fig. 2

Technology roadmap"

Fig. 3

Training area and verification area"

Table 2

Classification results of raw data set"

监督分类模型
Supervised classifiers		 训练集识别率
Accuracy of training set (%)		 测试集 Test set
准确率 Accuracy(%) 精确率 Precision(%) 召回率 Recall (%) F1 (%)
支持向量机 SVM 98.76 98.48 98.49 98.49 98.48
K-最近邻 KNN 94.78 93.94 94.14 93.94 93.92
Cart决策树 Cart 95.40 91.41 91.59 91.41 91.39
随机森林 RF 99.13 96.46 96.53 96.47 96.46
人工神经网络 ANN 97.67 99.00 99.02 99.00 99.00

Table 3

Classification results of Relief subset"

Relief特征子集
Relief subset		 训练集识别率
Accuracy of training set (%)		 测试集 Test set
准确率 Accuracy (%) 精确率 Precision (%) 召回率 Recall (%) F1 (%)
支持向量机 SVM 96.77 97.22 97.23 97.22 97.22
K-最近邻 KNN 100 93.43 93.63 93.43 93.41
Cart决策树 CART 93.91 90.66 91.25 90.66 90.58
随机森林 RF 97.14 92.93 93.02 92.93 92.92
人工神经网络 ANN 95.44 96.67 96.69 96.67 96.67

Table 4

Classification results of SVM-RFE subset"

SVM-RFE特征子集
SVM-RFE subset		 训练集识别率
Accuracy of training set (%)		 测试集 Test set
准确率 Accuracy (%) 精确率 Precision (%) 召回率 Recall (%) F1 (%)
支持向量机 SVM 98.76 98.23 98.23 98.23 98.23
K-最近邻 KNN 100 95.96 96.07 95.96 95.95
Cart决策树 CART 97.64 94.44 94.65 94.44 94.43
随机森林 RF 99.38 97.22 97.25 97.22 97.22
人工神经网络 ANN 97.78 99.00 99.02 99.00 99.00

Fig. 4

Classification result of verified regions a. RF, SVM & ANN classification results of all features in validation area; b. RF, SVM & ANN classification results of Relief subset in validation area; c. RF, SVM & ANN classification results of SVM-RFE subset in validation area"

Table 5

Confusion matrix of verify area with all features"

全部特征 All features 地物 Objects 玉米 Maize (%) 杂草 Weed (%) Kappa系数 Kappa coefficient 总体精度 Overall accuracy (%)
随机森林
RF		 玉米 Maize 88.47 4.92 0.82 90.95
杂草 Weed 11.53 95.08
支持向量机
SVM		 玉米 Maize 89.69 4.29 0.84 91.94
杂草 Weed 10.31 95.71
人工神经网络
ANN		 玉米 Maize 78.21 5.08 0.70 84.47
杂草 Weed 21.79 94.92

Table 6

Confusion matrix of verify area with Relief subset"

Relief特征子集
Relief subset		 地物
Objects		 玉米
Maize (%)		 杂草
Weed (%)		 Kappa系数
Kappa coefficient		 总体精度
Overall accuracy (%)
随机森林
RF		 玉米 Maize 86.03 5.32 0.79 89.27
杂草 Weed 13.97 94.68
支持向量机
SVM		 玉米 Maize 85.55 4.88 0.79 89.13
杂草 Weed 14.45 95.12
人工神经网络
ANN		 玉米 Maize 77.06 7.38 0.67 82.89
杂草 Weed 22.94 92.62

Table 7

Confusion matrix of verify area with SVM-RFE subset"

SVM-RFE特征子集
SVM-RFE subset		 地物
Objects		 玉米
Maize (%)		 杂草
Weed (%)		 Kappa系数
Kappa coefficient		 总体精度
Overall accuracy (%)
随机森林
RF		 玉米 Maize 87.99 5.13 0.81 90.56
杂草 Weed 12.01 94.87
支持向量机
SVM		 玉米 Maize 87.85 4.36 0.82 90.76
杂草 Weed 12.15 95.64
人工神经网络
ANN		 玉米 Maize 82.62 6.30 0.74 86.77
杂草 Weed 17.38 93.70
[1] 翁凌云 . 我国玉米生产现状及发展对策分析. 中国食物与营养, 2010(1):22-25.
WENG L Y . Status of corn production in China and its countermeasures. Food and Nutrition in China, 2010(1):22-25. (in Chinese)
[2] LOUARGANT M, VILLETTE S, JONES G, VIGNEAU N, PAOLI J, GÉE C . Weed detection by UAV: Simulation of the impact of spectral mixing in multispectral images. Precision Agriculture, 2017,18:932-951.
[3] 山东省农业农村厅. 山东省到2020年农药使用量零增长行动方案. 山东农药信息, 2015(4):13-14.
Shandong Provincial Department of Agricultural and Rural Affairs. Shandong province issued the action plan for zero increase of pesticide use in Shandong province by 2020. Shandong Pesticide News, 2015(4):13-14. (in Chinese)
[4] 周志艳, 明锐, 臧禹, 何新刚, 罗锡文, 兰玉彬 . 中国农业航空发展现状及对策建议. 农业工程学报, 2017,33(20):1-13.
ZHOU Z Y, MING R, ZANG Y, HE X G, LUO X W, LAN Y B . Development status and countermeasures of agricultural aviation in China. Transactions of the Chinese Society of Agricultural Engineering, 2017,33(20):1-13. (in Chinese)
[5] 兰玉彬 . 精准农业航空技术现状及未来展望. 农业工程技术, 2017,37(30):27-30.
LAN Y B . Current status and future prospects of precision agricultural aviation technology. Agricultural Engineering Technology, 2017,37(30):27-30. (in Chinese)
[6] 何东健, 何勇, 李明赞, 洪添胜, 王成红, 宋苏, 刘允刚 . 精准农业中信息相关科学问题研究进展. 中国科学基金, 2011,25(1):10-16.
HE D J, HE Y, LI M Z, HONG T S, WANG C H, SONG S, LIU Y G . Research progress of information science-related problems in precision agriculture. Bulletin of National Natural Science Foundation of China, 2011,25(1):10-16. (in Chinese)
[7] 兰玉彬, 王国宾 . 中国植保无人机的行业发展概况和发展前景. 农业工程技术, 2018,38(9):17-27.
LAN Y B, WANG G B . China's plant protection drone industry development overview and development prospects. Agricultural Engineering Technology, 2018,38(9):17-27. (in Chinese)
[8] 史舟, 梁宗正, 杨媛媛, 郭燕 . 农业遥感研究现状与展望. 农业机械学报, 2015,46(2):247-260.
SHI Z, LIANG Z Z, YANG Y Y, GUO Y . Status and prospect of agricultural remote sensing. Transactions of the Chinese Society for Agricultural Machinery, 2015,46(2):247-260. (in Chinese)
[9] 金小俊, 陈勇, 孙艳霞 . 农田杂草识别方法研究进展. 农机化研究, 2011,33(7):23-27.
JIN X J, CHEN Y, SUN Y X . Research advances of weed identification in agricultural fields. Journal of Agricultural Mechanization Research, 2011,33(7):23-27. (in Chinese)
[10] 毛文华, 张银桥, 王辉, 赵博, 张小超 . 杂草信息实时获取技术与设备研究进展. 农业机械学报, 2013,44(1):190-195.
MAO W H, ZHANG Y Q, WANG H, ZHAO B, ZHANG X C . Advance techniques and equipments for real-time weed detection. Transactions of the Chinese Society for Agricultural Machinery, 2013,44(1):190-195. (in Chinese)
[11] 邓向武, 齐龙, 马旭, 蒋郁, 陈学深, 刘云海, 陈伟烽 . 基于多特征融合和深度置信网络的稻田苗期杂草识别. 农业工程学报, 2018,34(14):165-172.
DENG X W, QI L, MA X, JIANG Y, CHEN X S, LIU Y H, CHEN W F . Recognition of weeds at seedling stage in paddy fields using multi-feature fusion and deep belief networks. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(14):165-172. (in Chinese)
[12] 孙俊, 何小飞, 谭文军, 武小红, 沈继峰, 路虎 . 空洞卷积结合全局池化的卷积神经网络识别作物幼苗与杂草. 农业工程学报, 2018,34(11):159-165.
SUN J, HE X F, TAN W J, WU X H, SHEN J F, LU H . Recognition of crop seedling and weed recognition based on dilated convolution and global pooling in CNN. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(11):159-165. (in Chinese)
[13] 何东健, 乔永亮, 李攀, 高瞻, 李海洋, 唐晶磊 . 基于SVM-DS多特征融合的杂草识别. 农业机械学报, 2013,44(2):182-187.
HE D J, QIAO Y L, LI P, GAO Z, LI H Y, TANG J L . Weed recognition based on SVM-DS multi-feature fusion. Transactions of the Chinese Society for Agricultural Machinery, 2013,44(2):182-187. (in Chinese)
[14] 潘冉冉, 骆一凡, 王昌, 张初, 何勇, 冯雷 . 高光谱成像的油菜和杂草分类方法. 光谱学与光谱分析, 2017,37(11):3567-3572.
PAN R R, LUO Y F, WANG C, ZHANG C, HE Y, FENG L . Classifications of oilseed rape and weeds based on hyperspectral imaging. Spectroscopy and Spectral Analysis, 2017,37(11):3567-3572. (in Chinese)
[15] 王海华, 朱梦婷, 李莉, 王丽燕, 赵海英, 梅树立 . 基于剪切波变换和无人机麦田图像的区域杂草识别方法. 农业工程学报, 2017,33(S1):99-106.
WANG H H, ZHU M T, LI L, WANG L Y, ZHAO H Y, MEI S L . Regional weed identification method from wheat field based on unmanned aerial vehicle image and shearlets. Transactions of the Chinese Society of Agricultural Engineering, 2017,33(S1):99-106. (in Chinese)
[16] 肖武, 任河, 吕雪娇, 闫皓月, 孙诗睿 . 基于无人机遥感的高潜水位采煤沉陷湿地植被分类. 农业机械学报, 2019,50(2):177-186.
XIAO W, REN H, LÜ X J, YAN H Y, SUN S R . Vegetation classification by using UAV remote sensing in coal mining subsidence wetland with high ground-water level. Transactions of the Chinese Society for Agricultural Machinery, 2019,50(2):177-186. (in Chinese)
[17] NOBUYUKI O . A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 1979,1(1):62-66.
[18] 孙滨峰, 赵红, 陈立才, 舒时富, 叶春, 李艳大 . 基于植被指数选择算法和决策树的生态系统识别. 农业机械学报, 2019,50(6):194-200.
SUN B F, ZHAO H, CHEN L C, SHU S F, YE C, LI Y D . Identification of ecosystems based on vegetation indices selection algorithm and decision tree. Transactions of the Chinese Society for Agricultural Machinery, 2019,50(6):194-200. (in Chinese)
[19] 刘畅, 杨贵军, 李振海, 汤伏全, 王健雯, 张春兰, 张丽妍 . 融合无人机光谱信息与纹理信息的冬小麦生物量估测. 中国农业科学, 2018,51(16):3060-3073.
LIU C, YANG G J, LI Z H, TANG F Q, WANG J W, ZHANG C L, ZHANG L Y . Biomass estimation in winter wheat by UAV spectral information and texture information fusion. Scientia Agricultura Sinica, 2018,51(16):3060-3073. (in Chinese)
[20] ROUSE J W, HAAS R W, SCHELL J A, DEERING D W, HARLAN J C . Monitoring the vernal advancement and retrogradation (Greenwave effect) of natural vegetation Final Rep. RSC 1978-4. Remote Sensing Center, Texas A&M University, College Station, 1974.
[21] JORDAN C F . Derivation of leaf-area index from quality of light on the forest floor. Ecology, 1969,50:663-666.
[22] PEARSON R L, MILLER L D . Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie. Proceedings of the 8th International Symposium on Remote Sensing of Environment. Environmental Research Institute of Michigan. Ann Arbor, MI, USA, 1972: 1357-1381.
[23] HUETE A R . A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 1988,25:295-309.
[24] RONDEAUX G, STEVEN M, BARET F . Optimization of soil- adjusted vegetation indices. Remote Sensing of Environment, 1996,55:95-107.
[25] GITELSON A A, KAUFMAN Y J, MERZLYAK M N . Use of a green channel in remote sensing of global vegetation from EOS- MODIS. Remote Sensing of Environment, 1996,58:289-298.
[26] HUETE A, DIDAN K, MIURA T, RODRIGUEZ E P, GAO X, FERREIRA L G . Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 2002,83:195-213.
[27] 周志华 . 机器学习. 北京: 清华大学出版社, 2017.
ZHOU Z H . Machine Learning. Beijing: Tsinghua University Press, 2017. (in Chinese)
[28] 李慧, 祁力钧, 张建华, 冀荣华 . 基于PCA-SVM的棉花出苗期杂草类型识别. 农业机械学报, 2012,43(9):184-189, 196.
LI H, QI L J, ZHANG J H, JI R H . Recognition of weed during cotton emergence based on principal component analysis and support vector machine. Transactions of the Chinese Society for Agricultural Machinery, 2012,43(9):184-189, 196. (in Chinese)
[29] 李俊秀, 姜三平 . 基于主成分分析的图像自适应阈值去噪算法. 红外技术, 2014,36(4):311-314, 319.
LI J X, JIANG S P . Adaptive threshold image denoising algorithm based on principal component analysis. Infrared Technology, 2014,36(4):311-314, 319. (in Chinese)
[30] HARALICK R M, SHANMUGAM K . Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 1973,3(6):610-621.
[31] 吴文涵, 陶华敏, 肖山竹, 汤朋文 . 灰度共生矩阵纹理特征提取算法的优化与实现. 数字技术与应用, 2015(6):124-126.
WU W H, TAO H M, XIAO S Z, TANG P W . Optimization and implementation of texture feature extraction algorithm for gray level co-occurrence matrix. Digital Technology and Application, 2015(6):124-126. (in Chinese)
[32] 韩文霆, 孙瑜, 徐腾飞, 陈香维, SU K O . 基于RGB图像处理的玉米叶片含水率监测方法研究. 农业工程技术, 2016,36(12):75.
HAN W T, SUN Y, XU T F, CHEN X W, SU K O . Detecting maize leaf water status by using digital RGB images. Agricultural Engineering Technology, 2016,36(12):75. (in Chinese)
[33] 侯群群, 王飞, 严丽 . 基于灰度共生矩阵的彩色遥感图像纹理特征提取. 国土资源遥感, 2013,25(4):26-32.
HOU Q Q, WANG F, YAN L . Extraction of color image texture feature based on gray-level co-occurrence matrix. Remote Sensing for Land & Resources, 2013,25(4):26-32. (in Chinese)
[34] 韩文霆, 李广, 苑梦婵, 张立元, 师志强 . 基于无人机遥感技术的玉米种植信息提取方法研究. 农业机械学报, 2017,48(1):139-147.
HAN W T, LI G, YUAN M C, ZHANG L Y, SHI Z Q . Extraction method of maize planting information based on UAV remote sensing technology. Transactions of the Chinese Society for Agricultural Machinery, 2017,48(1):139-147. (in Chinese)
[35] 黄晓娟, 张莉 . 改进的多类支持向量机递归特征消除在癌症多分类中的应用. 计算机应用, 2015,35(10):2798-2802.
HUANG X J, ZHANG L . Modified multi-class support vector machine recursive feature elimination for cancer multi-classification. Journal of Computer Applications, 2015,35(10):2798-2802. (in Chinese)
[36] 王君 . 基于SVM-RFE的特征选择方法研究[D]. 大连: 大连理工大学, 2015.
WANG J . The research of feature selection algorithm based on SVM- RFE[D]. Dalian: Dalian University of Technology, 2015. (in Chinese)
[37] 蒋玉娇, 王晓丹, 王文军, 毕凯 . 一种基于PCA和ReliefF的特征选择方法. 计算机工程与应用, 2010,46(26):170-172.
JIANG Y J, WANG X D, WANG W J, BI K . New feature selection approach by PCA and ReliefF. Computer Engineering and Applications, 2010,46(26):170-172. (in Chinese)
[38] 戴建国, 张国顺, 郭鹏, 曾窕俊, 崔美娜, 薛金利 . 基于无人机遥感可见光影像的北疆主要农作物分类方法. 农业工程学报, 2018,34(18):122-129.
DAI J G, ZHANG G S, GUO P, ZENG T J, CUI M N, XUE J L . Classification method of main crops in northern Xinjiang based on UAV visible waveband images. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(18):122-129. (in Chinese)
[1] ZHANG Xiao-Dong, MAO Han-Ping, ZUO Zhi-Yu, SUN Jun, ZHANG Hong-Tao. Multi-Spectral Images Estimation Models for Nitrogen Contents of Rape [J]. Scientia Agricultura Sinica, 2011, 44(16): 3323-3332.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备09089781号-30
Copyright 2018 © Editorial office of Scientia Agricultura Sinica
Tel: 86-010-82106279    E-mail: zgnykx@mail.caas.net.cn
Supported by Beijing Magtech Co.Ltd