作物种植行自动检测研究现状与趋势

doi:10.3864/j.issn.0578-1752.2021.13.004

Abstract

Abstract:

Field crops are generally planted in rows to improve planting efficiency and facilitate field management. Therefore, the automatic detection of crop planting rows is of great significance for intelligent agricultural machinery carrying sensors to capture images to achieve autonomous navigation and precision spray pesticide, and for drones equipped with sensors to capture high-resolution images to generating field precision management zones. It is an important part of smart agriculture. This research first systematically summarized the existing crop planting row automatic detection methods, and illustrated the basic principles, development status and advantages and disadvantages of the Hough transform method, least square method, green pixel accumulation method, Blob analysis method, filtering method, and vanishing point method for crop planting row detection. Secondly, considering the previous researches, this study proposed some scientific and technical issues that needed to be study in future, such as how different spatial and spectral resolution images affected the detection accuracy of crop planting rows; how to detection of crop planting rows with different spatial distribution features and then perform accurate mapping of crop growth status based on drone images; how to establish a standardized crop planting row detection process. Finally, based on the current status and existing problems of planting row detection technology, several research directions were suggested, including crop planting row detection technology that could adapt to complex environments, such as high weed pressure, to improve the accuracy of autonomous navigation of intelligent agricultural machinery; the method of accurately mapping crop growth status based on the results of planting row recognition and then delineating of field precision management zones; coupling crop growth monitoring technology from drone images and intelligent agricultural machinery to make field precision management. This article would provide valuable references for automatic detection of crop planting rows in images and its related application researches.

Key words: plant row, crop, automatic detection, status, trends

CHEN PengFei,MA Xiao. Research Status and Trends of Automatic Detection of Crop Planting Rows[J].Scientia Agricultura Sinica, 2021, 54(13): 2737-2745.

Figures/Tables 1

Table 1

Principles, advantages and disadvantages, and application scope for different crop planting row detection method"

分类方法 Class methods	基本原理 Principle	优缺点 Advantage and disadvantage	适用范围 Application scope
Hough变换法 Hough transform method	将图像空间的点变换为参数空间中的线,统计参数空间的峰值点,最终将峰值点按一定映射关系在图像空间中形成相应的种植行 Transform the points in the image space into lines in the parameter space, and then count the peak points in the parameter space, and finally form the corresponding planting rows in the image space based on peak points according to the certain relationship between the two spaces	能同时进行图像中多条种植行的提取,但也存在计算量大、容易受影像中杂草像元干扰等缺点 It has advantage that can extract multiple planting rows in the image at the same time, but it also has disadvantages such as long calculation time and easy to be affected by weed pixels	杂草压力小、数据量小的影像中作物种植行提取;可同时在无人机影像及田间农机拍摄影像中应用 It is suitable for planting row detection in images with a low weed pressure and small data volume; It can be used with both drone images and images taken from agricultural machinery
最小二乘法 Least square method	基于获取的种植行特征点,利用最小二乘拟合的方式获取种植行 Based on the obtained represent points of planting row, the least squares fitting method is used to make the planting row	具有算法简单、计算速度快等优势,但存在容易受图像中杂草像元干扰的缺点 It has advantages of simple algorithm and fast calculation speed, but it also has disadvantage of being easily affected by weed pixels	杂草压力小的影像中作物种植行提取;可同时在无人机影像及田间农机拍摄影像中应用 It is suitable for planting row detection in images with a low weed pressure; It can be used with both drone images and images taken from agricultural machinery
绿色像元累加法 Green pixel accumulation method	假定沿作物行方向上会有最大的植被绿色像元累积,据此提取作物种植行 The crop planting row is detected according to the assumption that there will be the largest accumulation of vegetation green pixels along the direction of the crop row	具有算法简单、计算速度快等优势,但存在容易受图像中杂草像元干扰的缺点 It has advantages of simple algorithm and fast calculation speed, but it also has disadvantage of being easily affected by weed pixels	杂草压力小的影像中作物种植行提取;可同时在无人机影像及田间农机拍摄影像中应用 It is suitable for planting row detection in images with a low weed pressure; It can be used with both drone images and images taken from agricultural machinery
Blob分析法 Blob analysis method	假设图中一个植被的连通区域即代表一个作物行,可以通过Blob分析确定每一个作物行连通区域的重心与长轴,从而生成种植行 Assuming that a connected area of vegetation in the image represents a crop row, Blob analysis can be used to determine gravity and long axis of each connected area to generate planting row	能耐受一定的杂草压力,但要求种植行必须呈直线分布 It has advantage that can endure a certain weed pressure, it also has disadvantage that require planting rows must be linearly distributed	杂草压力不大且影像中作物行呈直线的种植行提取;可同时在无人机影像及田间农机拍摄影像中应用 It is suitable for planting row detection in images with a low weed pressure and linearly distributed; It can be used with both drone images and images taken from agricultural machinery
滤波法 Filtering method	假定作物种植行呈特定的特征和图案,这些特征和图案可以用函数描述,基于函数来确定种植行位置 Assuming that the crop planting rows have specific features and patterns and these features and patterns can be described by function, the planting row can be determined based on the function	具有不易受杂草干扰的优势,但要求作物种植行按一定规律出现,当不存在这种规律时会产生误差 It has advantage of not being easily affected by weed pixels, but it also has disadvantage that require crop planting rows must distribute with a certain rule, and errors will occur if there exist no such rule	影像中作物种植行分布呈现规律分布;可同时在无人机影像及田间农机拍摄影像中应用 It is suitable for planting row detection that are distributed with a certain rule in images; It can be used with both drone images and images taken from agricultural machinery
消隐点法 Vanishing point method	田间拍摄影像近处的种植行显得间隔比较大,而远处的显得间隔比较小,如果把图像中作物种植行进行延长,它们会交于一点（即消隐点）,根据这一特征可进行作物种植行的提取 In the field image, planning rows in the vicinity appear to be more spaced, while those in the distance appear to be less spaced. If extend crop rows in the field image, they will meet at one point (vanishing point). According to this feature, crop planting row can be detected	具有计算速度快、能耐受一定杂草压力的优势,但要求种植行必须呈直线分布,且图像中种植行存在“消隐点” It has advantage that can endure a certain weed pressure and has a fast calculation speed, it also has disadvantage that require planting rows must be linearly distributed and have vanishing point in the image	杂草压力不大且影像中作物行呈直线并存在“消隐点”的种植行提取;仅适于在田间农机拍摄影像中应用 It is suitable for planting row detection with low weed pressure and the rows in image must be linear distributed and have vanishing point; It only can be used with images taken from agricultural machinery

Table 1

References 50

[1]	陈鹏飞. 无人机在农业中的应用现状与展望. 浙江大学学报(农业与生命科学版), 2018, 44(4):399-406.
	CHEN P F. Applications and trends of unmanned aerial vehicle in agriculture. Journal of Zhejiang University (Agriculture and Life Sciences), 2018, 44(4):399-406. (in Chinese)
[2]	赵春江. 智慧农业发展现状及战略目标研究. 智慧农业, 2019, 1(1):1-7.
	ZHAO C J. State-of-art and recommended developmental strategic objective of smart agriculture. Smart Agriculture, 2019, 1(1):1-7. (in Chinese)
[3]	邝继双, 汪懋华. 3S技术在农田基础地图测绘与更新中的集成应用. 农业工程学报, 2003, 19(3):220-223.
	KUANG J S, WANG M H. Application of GIS, GPS and RS for field surveying, mapping and data updating. Transactions of the Chinese Society of Agricultural Engineering, 2003, 19(3):220-223. (in Chinese)
[4]	INTRIERI C, PONI S, REBUCCI B. Row orientation effects on whole-canopy gas exchange of potted and field-grown grapevines. Vitis, 1998, 37(4):147-154.
[5]	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
[6]	TORRES-SÁNCHEZ J, LÓPEZ-GRANADOS F, PEÑA-BARRAGÁN J M. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture, 2015, 114:43-52. doi: 10.1016/j.compag.2015.03.019
[7]	DE CASTRO A I, TORRES-SÁNCHEZ J, PEÑA J M, JIMÉNEZ- BRENES F M, CSILLIK O, LÓPEZ-GRANADOS F. An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV Imagery. Remote Sensing, 2018, 10(2):285. doi: 10.3390/rs10020285
[8]	DE SOUZA C H W, LAMPARELLI R A C, ROCHA J V, MAGALH ÃES P S G. Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images. Computers and Electronics in Agriculture, 2017, 143:49-56. doi: 10.1016/j.compag.2017.10.006
[9]	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):1-5. doi: 10.1109/LGRS.2017.2781679
[10]	姜国权, 杨小亚, 王志衡, 刘红敏. 基于图像特征点粒子群聚类算法的麦田作物行检测. 农业工程学报, 2017, 33(11):165-170.
	JIANG G Q, YANG X Y, WANG Z H, LIU H M. Crop rows detection based on image characteristic point and particle swarm optimization- clustering algorithm. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(11):165-170. (in Chinese)
[11]	姜国权, 柯杏, 杜尚丰, 张漫, 陈娇. 基于机器视觉的农田作物行检测. 光学学报, 2009, 29(4):1015-1020.
	JIANG G Q, KE X, DU S F, ZHANG M, CHEN J. Crop row detection based on machine vision. Acta Optica Sinica, 2009, 29(4):1015-1020. (in Chinese)
[12]	VIDOVIĆ I, CUPEC R, HOCENSKI Ž. Crop row detection by global energy minimization. Pattern Recognition, 2016, 55:68-86. doi: 10.1016/j.patcog.2016.01.013
[13]	谭衢霖, JOHANSEN S. 基于像元和对象分类的城区植被高分辨率遥感制图比较研究. 应用基础与工程科学学报, 2011, 19(3):441-448.
	TAN Q L, JOHANSEN S. Evaluation of urban vegetation mapping using high spatial resolution image: pixel versus object classification comparison. Journal of Basic Science and Engineering, 2011, 19(3):441-448. (in Chinese)
[14]	钦伟瑾. 基于对象的城市遥感影像分类方法及应用研究——以西安市阎良区为例[D]. 西安:长安大学, 2009.
	QING W J. The object-oriented classification of urban remote sensing images and the application research: Take Xi’an Yanliang district for example[D]. Xi’an: Chang An University, 2009. (in Chinese)
[15]	MENEGUZZO D M, LIKNES G C, NELSON M D. Mapping trees outside forests using high-resolution aerial imagery: A comparison of pixel- and object-based classification approaches. Environmental Monitoring and Assessment, 2013, 185:6261-6275. doi: 10.1007/s10661-012-3022-1
[16]	李石华, 王金亮, 毕艳, 陈姚, 朱妙园, 杨帅, 朱佳. 遥感图像分类方法研究综述. 国土资源遥感, 2005, 64(2):1-6.
	LI S H, WANG J L, BI Y, CHEN Y, ZHU M Y, YANG S, ZHU J. A review of methods for classification of remote sensing images. Remote Sensing for Land and Resources, 2005, 64(2):1-6. (in Chinese)
[17]	IMANI M, GHASSEMIAN H. An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges. Information Fusion, 2020, 59(2):59-83. doi: 10.1016/j.inffus.2020.01.007
[18]	龚健雅, 钟燕飞. 光学遥感影像智能化处理研究进展. 遥感学报, 2016, 20(5):733-747.
	GONG J Y, ZHONG Y F. Survey of intelligent optical remote sensing image processing. Journal of Remote Sensing, 2016, 20(5):733-747. (in Chinese)
[19]	HOUGH P V C. Method and means for recognizing complex patterns. US Patent 3069654. 1962-12-18.
[20]	张志斌, 罗锡文, 周学成, 臧英. 基于Hough变换和Fisher准则的垄线识别算法. 中国图象图形学报, 2007, 12(12):2164-2168.
	ZHANG Z B, LUO X W, ZHOU X C, ZANG Y. Crop rows detection based on hough transform and fisher discriminat criterion function. Journal of Image and Graphics, 2007, 12(12):2164-2168. (in Chinese)
[21]	BAKKER T, WOUTERS H, ASSELT K, BONTSEMA J, TANG L, MULLER J, STRATEN G. A vision based row detection system for sugar beet. Computers and Electronics in Agriculture, 2007, 60:87-95. doi: 10.1016/j.compag.2007.07.006
[22]	何洁, 孟庆宽, 张漫, 仇瑞承, 项明, 杜尚丰. 基于边缘检测与扫描滤波的农机导航基准线提取方法. 农业机械学报, 2014, 45(S1):265-270.
	HE J, MEN Q K, ZHANG M, QIU R C, XIANG M, DU S F. Crop baseline extraction method for off-road vehicle based on boundary detection and scan-filter. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(S1):265-270. (in Chinese)
[23]	ASTRAND B, BAERVELDT A J. A vision based row-following system for agricultural field machinery. Mechatronics, 2005, 15(2):251-269. doi: 10.1016/j.mechatronics.2004.05.005
[24]	OLIVERIRA 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.8859
[25]	苏伟, 蒋坤萍, 闫安, 刘哲, 张明政, 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)
[26]	WEISS M, BARET F. Using 3D point clouds derived from UAV RGB imagery to describe vineyard 3D macro-structure. Remote Sensing, 2017, 9(2):111. doi: 10.3390/rs9020111
[27]	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 randomize method. Transactions of the Chinese Society for Agricultural Machinery, 2008, 39(11):85-88, 93. (in Chinese)
[28]	孟笑天, 徐艳蕾, 王新东, 何润, 翟钰婷. 基于改进K均值特征点聚类算法的作物行检测. 农机化研究, 2020, 42(8):26-30.
	MENG X T, XU Y L, WANG X D, HE R, ZHAI Y T. Crop line detection based on improved K-means feature point clustering algorithm. Journal of Agricultural Mechanization Research, 2020, 42(8):26-30. (in Chinese)
[29]	ZHANG X, LI X, ZHANG B, ZHOU J, TIAN G, XIONG Y, GU B. 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
[30]	WINTERHALTER W, VERONIKA F, DORNHEGE C, BURGARD W. Crop row detection on tiny plants with the pattern Hough transform. IEEE Robotics and Automation Letters, 2018, 3(4):3394-3401. doi: 10.1109/LSP.2016.
[31]	亢洁, 马振. 基于轮廓查找与线扫描的作物行检测算法. 科学技术与工程, 2019, 19(20):273-277.
	KANG J, MA Z. Detection algorithm of crop row based on contour searching and line scanning. Science Technology and Engineering, 2019, 19(20):273-277. (in Chinese)
[32]	GARCĬA-SANTILLÁN I, GUERRERO J M, MONTALVO M, PAJARES G. Curved and straight crop row detection by accumulation of green pixels from images in maize fields. Precision Agriculture, 2017, 19:18-41. doi: 10.1007/s11119-016-9494-1
[33]	LÓPEZ-GRANADOS F, TORRES-SÁNCHEZ J, SERRANO-PÉREZ A, DE CASTRO A I, MESAS-CARRASCOSA F J, PEÑA J M. Early season weed mapping in sunflower using UAV technology: Variability of herbicide treatment maps against weed thresholds. Precision Agriculture, 2016. 17:183-199. doi: 10.1007/s11119-015-9415-8
[34]	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. doi: 10.5194/isprs-archives-XLII-2-W13-349-2019
[35]	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
[36]	赵慧. 基于Blob的运动目标检测与跟踪算法研究[D]. 哈尔滨: 哈尔滨工业大学, 2017.
	ZHAO H. Object detection and tracking algorithm based on blob[D]. Harbin: Harbin Institute of Technology, 2017. (in Chinese)
[37]	FOBTAINE V, CROWE T. Development of line-detection algorithm for local positioning in densely seeded crops. Canadian Biosystems Engineering, 2006, 48(7):19-29.
[38]	PEÑA J M, TORRES-SÁNCHEZ J, DE CASTRO A I, KELLY M, LÓPEZ-GRANADOS F. Weed Mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. Plos One, 2013, 8(10):e77151. doi: 10.1371/journal.pone.0077151
[39]	GARCĬA-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
[40]	HAGUE T, TILLETT N D. A bandpass filter-based approach to crop row location and tracking. Mechatronics, 2001, 11(1):1-12. doi: 10.1016/S0957-4158(00)00003-9
[41]	BOSSU J, GEE C, JONES G, TRUCHETET F. Wavelet transform to discriminate between crop and weed in perspective agronomic images. Computers and Electronics in Agriculture, 2009, 65:133-143. doi: 10.1016/j.compag.2008.08.004
[42]	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):2429-2441. doi: 10.1016/j.eswa.2014.10.033
[43]	KISE M, ZHANG Q. Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance. Biosystems Engineering, 2008, 101(2):191-198. doi: 10.1016/j.biosystemseng.2008.08.001
[44]	VIOIX J B, DOUZALS J P, TRUCHETET F, ASSÉMAT L, GUILLEMIN J. Spatial and spectral methods for weed detection and localization. EURASIP Journal on Advances in Signal Processing, 2002, 7:679-685.
[45]	DE SOUZA C H W, LAMPARELLI R A C, ROCHA J V, MAGALHAES P S G. Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images. Computers and Electronics in Agriculture, 2017, 143:49-56. doi: 10.1016/j.compag.2017.10.006
[46]	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
[47]	ROMEO J, PAJARES G, MONTALVO M, GUERRERO J M, GUIJARRO M, RIBERIRO A. Crop row detection in maize fields inspired on the human visual perception. The Scientific World Journal, 2012, 2012(1):484390.
[48]	王晓杰. 基于机器视觉的农田作物行检测方法研究[D]. 焦作: 河南理工大学, 2016.
	WANG X J. Study on crop rows detection with machine vision[D]. Jiaozuo: Henan Polytechnic University, 2016. (in Chinese)
[49]	GUERRERO J M, GUIJARRO M, MONTALVO M, ROMEO J, EMMI L, RIBEIRO A, PAJARES G. Automatic expert system based on images for accuracy crop row detection in maize fields. Expert Systems with Applications, 2013, 40(2):656-664. doi: 10.1016/j.eswa.2012.07.073
[50]	汪沛, 罗锡文, 周志艳, 臧英, 胡炼. 基于微小型无人机的遥感信息获取关键技术综述. 农业工程学报, 2014, 30(18):1-12.
	WANG P, LUO X W, ZHOU Z Y, ZANG Y, HU L. Key technology for remote sensing information acquisition based on micro UAV. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(18):1-12. (in Chinese)

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