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

摘要/Abstract

摘要：

大田作物一般成行种植,以提高种植效率和方便田间管理。因此,作物种植行自动检测对于智能农机携带传感器拍摄影像实现自主导航、精准打药,乃至基于无人机搭载传感器拍摄高分辨率影像生成田间的精准管理作业单元都具有重要意义,是智慧农业管理的重要组成部分。本研究首先系统归纳总结了已有作物种植行自动检测方法,分析了Hough变换法、最小二乘法、绿色像元累积法、Blob分析法、滤波法、消隐点法等作物种植行提取方法的基本原理、发展现状与优、缺点;其次,针对已有研究,提出目前还存在的、需要探讨的科学技术问题,比如不同空间和光谱分辨率影像如何影响作物种植行提取的精度;怎样基于无人机识别不同空间分布特征的作物种植行并进行长势空间精准制图;如何构建标准化的作物种植行识别技术流程等;最后,针对种植行提取技术现状与存在的问题,提出未来的若干研究方向,包括能适应高杂草压力等复杂环境的作物种植行精准识别技术,以提高智能农机自主导航精度;能基于种植行识别结果进行作物长势精准制图,从而支撑田间精准分区的方法;耦合无人机遥感精准作物长势监测与智能农机作业的田间精准管理技术等。本文可为影像中作物种植行自动提取及其相关应用研究提供参考。

关键词: 种植行, 作物, 自动检测, 现状, 趋势

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

陈鹏飞,马啸. 作物种植行自动检测研究现状与趋势[J]. 中国农业科学, 2021, 54(13): 2737-2745.

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

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链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2021.13.004

https://www.chinaagrisci.com/CN/Y2021/V54/I13/2737

图/表 1

表1

不同作物种植行提取方法的基本原理、优缺点及适用范围"

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

表1

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