Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (20): 3926-3938.doi: 10.3864/j.issn.0578-1752.2022.20.005

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

Improvement of Row Detection Method Before Wheat Canopy Closure Using Multispectral Images of UAV Image

MA Xiao1,2(),CHEN PengFei1,3()   

  1. 1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences/State Key Laboratory of Resources and Environment Information System, Beijing 100101
    2University of Chinese Academy of Science, Beijing 100049
    3National Earth System Science Data Center, National Science and Technology Infrastructure of China, Beijing 100101
  • Received:2021-12-10 Accepted:2022-02-28 Online:2022-10-16 Published:2022-10-24
  • Contact: PengFei CHEN E-mail:maxiao20@mails.ucas.ac.cn;pengfeichen@igsnrr.ac.cn

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Abstract:

【Objective】In order to make precise management of wheat, it is of great significance to accurately identify the location of its rows. In this study, the aim was to improve the traditional Hough transform-based methods and the green pixel accumulation- based methods respectively, and then to analyze the different methods before and after the improvement for wheat row detection, so as to provide the technical support for accurate detection of wheat rows.【Method】A wheat water-nitrogen coupling experiment was established. During the early jointing stage, the multispectral images of wheat under different growth conditions was obtained by the RedEdge M sensors mounted on a four rotor unmanned aerial vehicle (UAV). First, based on these data, these images of the excess green minus excess red (ExGR) index were calculated, and then the Otsu method was performed on it to classify image pixels into vegetation and soil. Second, 3×1 line template was used for morphological opening operation to reduce boundary irregularity and remove noise. Third, according to the characteristics of the wheat rows in the image, the modified wheat row extraction methods were proposed based on the optimization of the peak detection process of the traditional Hough transform-based method and the angel detection process of the traditional green pixel accumulation-based method, respectively. Finally, the detection results before and after improving the two methods were compared with the result of visually interpreted wheat rows, and their performances were evaluated by using the detection rate and crop row detection accuracy (CRDA). 【Result】The combination of the ExGR index and OTSU method could accurately identify the vegetation pixels and soil pixels automatically, and the overall accuracy was 93.75% and the Kappa was 0.87. The morphological opening operation could remove the pattern noise and reduce the error of later crop row detection. The modified Hough transform-based method effectively improved the peak detection accuracy by restricting the peak detection range with prior knowledge; Compare with traditional Hough transform-based methods, the averaged detection rate has increased from 30% to 67%, and the averaged CRDA has increased from 0.22 to 0.44. The modified green pixel accumulation-based method investigated the accumulation characteristics of the green pixels of the entire image, which effectively improved the angel detection accuracy; Compare with traditional green pixel accumulation-based method, the averaged detection rate has increased from 14% to 93%, and the average CRDA has increased from 0.12 to 0.69. The identification accuracy of the four methods from high to low were the modified green pixel accumulation-based method, the modified Hough transform-based method, the traditional Hough transform-based method, and the traditional green pixel accumulation-based method. 【Conclusion】This study improved the traditional crop row detection methods, which provided the technical support for the identification of wheat rows with high planting density and small row spacing.

Key words: wheat, planting rows, Hough transform-based method, green pixel accumulation-based method, UAV image

Fig. 1

The winter wheat field experiment W1: 80% field capacity; W2: 60% field capacity; N1: 0; N2: 70 kg N·hm-2; N3: 140 kg N·hm-2; N4: 210 kg N·hm-2; N5: 280 kg N·hm-2"

Fig. 2

Unmanned aerial vehicle platform with one acquired image"

Fig. 3

Visual identified row line of wheat"

Fig. 4

Flow chart for Hough transform method before and after modification"

Fig. 5

Cumulative graph for the feature points of wheat rows on column orientation a: Unmanned aerial vehicle image of wheat;b: The image was rotated to make the direction of wheat row perpendicular to the direction of image row; c: The image was not rotated to make the direction of wheat row perpendicular to the direction of image row"

Fig. 6

Flow chart for green pixel accumulation method before and after modification"

Fig. 7

Unmanned aerial vehicle images of different nitrogen fertilization treatments a-e corresponding to N1-N5 nitrogen application amount"

Fig. 8

An example of the unmanned aerial vehicle image of a wheat plot and its corresponding vegetation/soil binary image a: Unmanned aerial vehicle image; b: Vegetation/soil binary image"

Table 1

Precision of classification of vegetation/soil"

分类结果
Classification result		 参考数据 Reference data
植被 Vegetation 土壤 Soil 总计 Summary 用户精度 User’s accuracy (%)
植被 Vegetation 1346 97 1443 93.28%
土壤 Soil 103 1654 1757 94.14%
总计 Summary 1449 1751 3200
生产者精度 Producer‘s accuracy (%) 92.89% 94.46%

Fig. 9

Vegetation/soil binary image before and after morphological operation a: Original vegetation/soil binary image; b: Morphological operation result"

Fig. 10

Results of identified feature points of wheat rows a: Vegetation/soil binary image; b: Feature point of wheat row"

Fig. 11

Detection rate and CRDA values of Hough transform method and green pixel accumulation method before and after modification under different water and nitrogen treatment a: Detection rate of Hough transform method before and after modification; b: CRDA values of Hough transform method before and after modification; c: Detection rate of green pixel accumulation method before and after modification; d: CRDA values of green pixel accumulation method before and after modification"

[1] 刘兆辉, 吴小宾, 谭德水, 李彦, 江丽华. 一次性施肥在我国主要粮食作物中的应用与环境效应. 中国农业科学, 2018, 51(20): 3827-3839.
LIU Z H, WU X B, TAN D S, LI Y, JIANG L H. Application and environmental effects of one-off fertilization technique in major cereal crops in China. Scientia Agricultura Sinica, 2018, 51(20): 3827-3839. (in Chinese)
[2] 霍李龙, 苗芳, 贾丽芳, 王长发. 种植方式对关中灌区冬小麦冠层光合及产量性状的影响. 麦类作物学报, 2017, 37(8): 1098-1104.
HUO L L, MIAO F, JIA L F, WANG C F. Effect of planting pattern on canopy photosynthesis and yield traits of winter wheat in the irrigation area of central Shaanxi. Journal of Triticeae Crops, 2017, 37(8): 1098-1104. (in Chinese)
[3] ZHANG X Y, LI X N, ZHANG B H, ZHOU J, TAIN G Z, XIONG Y J, GU B X. Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method. Computers and Electronics in Agriculture, 2018, 154: 165-175.
doi: 10.1016/j.compag.2018.09.014
[4] 李祥光, 赵伟, 赵雷雷. 缺株玉米行中心线提取算法研究. 农业工程学报, 2021, 37(18): 203-210.
LI X G, ZHAO W, ZHAO L L. Extraction algorithm of the center line of maize row in case of plants lacking. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(18): 203-210. (in Chinese)
[5] PLA F, SANCHIZ J M, MARCHANT J A, BRIVOT R. Building perspective models to guide a row crop navigation vehicle. Image and Vision Computing, 1997, 15(6): 465-473.
doi: 10.1016/S0262-8856(96)01147-X
[6] GARCÍA-SANTILLÁN I D, MONTALVO M, GUERRERO J M, PAJARES G. Automatic detection of curved and straight crop rows from images in maize fields. Biosystems Engineering, 2017, 156: 61-79.
doi: 10.1016/j.biosystemseng.2017.01.013
[7] 赵学观, 马伟, 高原源, 臧云飞, 何义川, 王秀. 基于逆透视变换的条播作物早期作物行识别. 江苏大学学报(自然科学版), 2019, 40(6): 668-675.
ZHAO X G, MA W, GAO Y Y, ZANG Y F, HE Y C, WANG X. Identification of early crop row for drillcrops based on reverse perspective transformation. Journal of Jiangsu University(Natural Science Edition), 2019, 40(6): 668-675. (in Chinese)
[8] 孙刚, 黄文江, 陈鹏飞, 高帅, 王秀. 轻小型无人机多光谱遥感技术应用进展. 农业机械学报, 2018, 49(3): 1-17.
SUN G, HUANG W J, CHEN P F, GAO S, WANG X. Advances in UAV-based multispectral remote sensing applications. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(3): 1-17. (in Chinese)
[9] PÉREZ-ORTIZ M, PEÑA J M, GUTIÉRREZ P A, TORRES- SÁNCHEZ J, HERVÁS-MARTÍNEZ C, LÓPEZ-GRANADOS F. A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Applied Soft Computing, 2015, 37: 533-544.
doi: 10.1016/j.asoc.2015.08.027
[10] 郑晓岚, 张显峰, 程俊毅, 任翔. 利用无人机多光谱影像数据构建棉苗株数估算模型. 中国图象图形学报, 2020, 25(3): 520-534.
ZHENG X L, ZHANG X F, CHENG J Y, REN X. Using the multispectral image data acquired by unmanned aerial vehicle to build an estimation model of the number of seeding stage cotton plants. Journal of Image and Graphics, 2020, 25(3): 520-534. (in Chinese)
[11] OLIVEIRA H C, GUIZILINI V C, NUNES I P, SOUZA J R. Failure detection in row crops from UAV images using morphological operators. IEEE Geoscience and Remote Sensing Letters, 2018, 15(7): 991-995.
doi: 10.1109/LGRS.2018.2819944
[12] HASSANEIN M, KHEDR M, EL-SHEIMY N. Crop row detection procedure using low-cost UAV imagery system. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019, XLII-2/W13: 349-356.
[13] PEÑA J M, TORRES-SÁNCHEZ J, SERRANO-PÉREZ A, DE CASTRO A I, LÓPEZ-GRANADOS F. Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors, 2015, 15(3): 5609-5626.
doi: 10.3390/s150305609 pmid: 25756867
[14] TENHUNEN H, PAHIKKALA T, NEVALAINEN O, TEUHOLA J, MATTILA H, TYYSTJÄRVI E. Automatic detection of cereal rows by means of pattern recognition techniques. Computers and Electronics in Agriculture, 2019, 162: 677-688.
doi: 10.1016/j.compag.2019.05.002
[15] CHEN P F, MA X, WANG F Y, LI J. A new method for crop row detection using unmanned aerial vehicle images. Remote Sensing, 2021, 13(17): 3526.
doi: 10.3390/rs13173526
[16] ZHAO C J, CHEN P F, HUANG W J, WANG J H, WANG Z J, JIANG A N. Effects of two kinds of variable-rate nitrogen application strategies on the production of winter wheat (Triticum aestivum). New Zealand Journal of Crop and Horticultural, 2009, 37(2): 149-155.
[17] OLSON D, CHATTERJEE A, FRANZEN D W, DAY S S. Relationship of drone-based vegetation indices with corn and sugarbeet yields. Agronomy Journal, 2019, 111(5): 2545-2557.
doi: 10.2134/agronj2019.04.0260
[18] SØGAARD H T, OLSEN H J. Determination of crop rows by image analysis without segmentation. Computer and Electronics in Agriculture, 2003, 38(2): 141-158.
doi: 10.1016/S0168-1699(02)00140-0
[19] HOUGH P V C. Method and means for recognizing complex patterns: US Patent 3069654. 1962-12-18 [2021-12-07].
[20] 苏伟, 蒋坤萍, 闫安, 刘哲, 张明郑, 王伟. 基于无人机遥感影像的育种玉米垄数统计监测. 农业工程学报, 2018, 34(10): 92-98.
SU W, JIANG K P, YAN A, LIU Z, ZHANG M Z, WANG W. Monitoring of planted lines for breeding corn using UAV remote sensing image. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(10): 92-98. (in Chinese)
[21] MEYER G E, NETO J C. Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, 2008, 63(2): 282-293.
doi: 10.1016/j.compag.2008.03.009
[22] KERKECH M, HAFIANE A, CANALS R. Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. Computers and Electronics in Agriculture, 2018, 155: 237-243.
doi: 10.1016/j.compag.2018.10.006
[23] 高铭阳, 张锦水, 潘耀忠, 段雅鸣, 张杜娟. 结合植被指数与作物高度反演冬小麦叶面积指数. 中国农业资源与区划, 2020, 41(8): 49-57.
GAO M Y, ZHANG J S, PAN Y Z, DUAN Y M, ZHANG D J. Retrieval of winter wheat leaf area index based on vegetation index and crop height. Chinese Journal of Agricultural Resources and Regional Planning, 2020, 41(8): 49-57. (in Chinese)
[24] OTSU N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 11: 62-66.
[25] 王宁, 陈方, 于博. 基于形态学开运算的面向对象滑坡提取方法研究. 遥感技术与应用, 2018, 33(3): 520-529.
WANG N, CHEN F, YU B. A object-oriented landslide extraction method based on morphological opening operation. Remote Sensing Technology and Application, 2018, 33(3): 520-529. (in Chinese)
[26] 姜国权, 柯杏, 杜尚丰, 陈娇. 基于机器视觉和随机方法的作物行提取算法. 农业机械学报, 2008, 39(11): 85-88, 93.
JIANG G Q, KE X, DU S F, CHEN J. Detection algorithm of crop rows based on machine vision and randomized method. Transactions of the Chinese Society for Agricultural Engineering, 2008, 39(11): 85-88, 93. (in Chinese)
[27] ZHAO H X, WANG Y X, SUN Z B, XU Q, LIANG D. Failure detection in eucalyptus plantation based on UAV images. Forests, 2021, 12(9): 1250.
doi: 10.3390/f12091250
[28] VIDOVIĆ I, CUPEC R, HOCENSKI Z. Crop row detection by global energy minimization. Pattern Recognition, 2016, 55: 68-86.
doi: 10.1016/j.patcog.2016.01.013
[29] JIANG G Q, WANG Z H, LIU H M. Automatic detection of crop rows based on multi-ROIs. Expert Systems with Applications, 2015, 42(5): 2492-2441.
[30] PUSDÁ-CHULDE M, ROBAYO A, GIUSTI A D, GARCÍA- SANTILLÁN I. Detection of crop lines and weeds in corn fields based on images obtained from a drone //NAIOUF M, RUCCI E, CHICHIZOLA F, DE GIUSTI L. Cloud Computing, Big Data & Emerging Topics. Cham: Springer, 2021: 31-45.
[31] BAH M D, HAFIANE A, CANALS R. Crownet: Deep network for crop row detection in UAV image. IEEE Access, 2020, 8: 5189-5200.
doi: 10.1109/ACCESS.2019.2960873
[32] 陈子文, 李伟, 张文强, 李云伍, 李明生, 李慧. 基于自动Hough变换累加阈值的蔬菜作物行提取方法研究. 农业工程学报, 2019, 35(22): 314-322.
CHEN Z W, LI W, ZHANG W Q, LI Y W, LI M S, LI H. Vegetable crop row extraction method based on accumulation threshold of Hough transformation. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(22): 314-322. (in Chinese)
[33] COMBA L, GAY P, PRIMICERIO J, AIMONINO D R. Vineyard detection from unmanned aerial systems images. Computers and Electronics in Agriculture, 2015, 114: 78-87.
doi: 10.1016/j.compag.2015.03.011
[34] 王侨, 孟志军, 付卫强, 刘卉, 张振国. 基于机器视觉的玉米苗期多条作物行线检测算. 农业机械学报, 2021, 52(4): 208-220.
WANG Q, MENG Z J, FU W Q, LIU H, ZHANG Z G. Detection algorithm of multiple crop row lines based on machine vision in maize seedling stage. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(4): 208-220. (in Chinese)
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