CN104949981A - Automatic detection method and system for cotton five-euphylla period - Google Patents
Automatic detection method and system for cotton five-euphylla period Download PDFInfo
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Abstract
本发明公开了一种棉花五真叶期检测方法,包括以下步骤:(1)采集定苗后棉田单排植株图像,将所述图像拆分成棉花单株的子图像,并进行颜色分割和作物图像分割,获得定苗后棉花植株子图像;(2)对于所有所述棉花植株子图像,检测植株边缘和骨架,获得植株主茎;(3)在主茎位置两侧,检测植株初侧茎,将两侧图像中的主茎上侧成锐角的初侧茎作为侧茎;(4)以侧茎和主茎的交点作为节点,当检测到2个或者2个以上节点的子图像数目占所有定苗后棉花植株子图像数目50%以上时,认为棉田进入五真叶期。本发明还提供了一种实现上述方法的系统。本发明检测结果准确,实时性强,实现自动观测,节省人力。
The invention discloses a method for detecting the five true leaf stages of cotton, which comprises the following steps: (1) collecting an image of a single row of plants in a cotton field after seedling setting, splitting the image into sub-images of a single cotton plant, performing color segmentation and crop Image segmentation to obtain sub-images of cotton plants after seedling setting; (2) For all the sub-images of cotton plants, detect plant edges and skeletons to obtain plant main stems; (3) detect initial side stems of plants on both sides of the main stem position, The primary side stems with an acute angle on the upper side of the main stem in the images on both sides are used as side stems; (4) The intersection point of the side stem and the main stem is used as a node, when the number of sub-images with 2 or more nodes is detected to account for all When the number of subimages of cotton plants after seedling setting is more than 50%, it is considered that the cotton field has entered the five true leaf stage. The present invention also provides a system for realizing the above method. The detection result of the invention is accurate, the real-time performance is strong, automatic observation is realized, and manpower is saved.
Description
技术领域technical field
本发明属于数字图像处理和农业气象观测交叉领域,更具体地,涉及一种棉花五真叶期自动检测方法及系统。The invention belongs to the intersection field of digital image processing and agricultural meteorological observation, and more specifically relates to a method and system for automatic detection of the five true leaf stages of cotton.
背景技术Background technique
棉花是我国主要的经济作物之一,中国的棉花产量也处于世界领先地位。棉花的五真叶期是棉花生长的一个重要环节,是农业气象观测的一个重要内容。Cotton is one of the main economic crops in China, and China's cotton output is also in the leading position in the world. The five true leaf stage of cotton is an important part of cotton growth and an important content of agricultural meteorological observation.
长期以来,主要采用人工观测记录的方式对棉花发育期相关信息进行记录,观测结果由于会受到观测员主观因素的影响,导致误差比较大;与此同时,由于棉花的生长周期较长,棉花种植的范围较广,单一地利用人工进行观测的方法耗时耗力。For a long time, the relevant information of the cotton development period has been recorded mainly by manual observation and recording. The observation results will be affected by the subjective factors of the observers, resulting in relatively large errors; at the same time, due to the long growth cycle of cotton, cotton planting The range is relatively wide, and the method of only using manual observation is time-consuming and labor-intensive.
发明内容Contents of the invention
针对现有技术的以上缺陷或改进需求,本发明提供了一种棉花五真叶期自动检测方法及系统,其目的在于通过图像处理方法分析棉田照片,从而判断棉花是否进入五真叶期,由此解决目前人工判断棉花五真叶期耗时耗力及不准确的技术问题。In view of the above defects or improvement needs of the prior art, the present invention provides a method and system for automatic detection of the five true leaf stages of cotton, the purpose of which is to analyze the photos of cotton fields by image processing methods, thereby judging whether cotton has entered the five true leaf stages, by This solves the current time-consuming, labor-intensive and inaccurate technical problems of manually judging the five true leaf stages of cotton.
为实现上述目的,按照本发明的一个方面,提供了一种棉花五真叶期自动检测方法,其特征在于,包括以下步骤:In order to achieve the above object, according to one aspect of the present invention, a method for automatic detection of the fifth true leaf stage of cotton is provided, which is characterized in that, comprising the following steps:
(1)获取定苗后棉花植株子图像:采集定苗后棉田单排植株正向前视图;用适合棉花植株单株图像大小的搜索框以一定的拆分步长,将所述图像拆分成子图像;对所述子图像进行颜色分割和作物图像分割,在作物分割方法的区域分割结果图上,保留颜色分割结果图中已检测到的像素所占据的区域,获得定苗后棉花植株子图像;(1) Acquire sub-images of cotton plants after seedling setting: collect the front view of a single row of plants in a cotton field after seedling setting; use a search box suitable for the image size of a single cotton plant to split the image into sub-images with a certain split step Carrying out color segmentation and crop image segmentation to the sub-image, on the area segmentation result map of the crop segmentation method, retain the area occupied by the detected pixels in the color segmentation result map, and obtain the cotton plant sub-image after seedling setting;
(2)检测棉花植株主茎:对于步骤(1)中获得的所有棉花植株子图像,采用边缘检测算法检测植株初边缘,采用骨架检测算法提取植株初骨架,对植株初边缘和植株初骨架进行链码检测和直线检测得到棉花植株竖直的植株边缘和植株骨架,将包含竖直植株骨架的竖直植株边缘内侧作为棉花植株主茎,获得包含植株主茎的棉花植株子图像;(2) Detect the main stem of cotton plants: For all the sub-images of cotton plants obtained in step (1), the edge detection algorithm is used to detect the initial edge of the plant, and the skeleton detection algorithm is used to extract the initial skeleton of the plant. Chain code detection and straight line detection obtain the vertical plant edge and the plant skeleton of the cotton plant, and the inside of the vertical plant edge that contains the vertical plant skeleton is used as the main stem of the cotton plant to obtain the sub-image of the cotton plant that includes the main stem of the plant;
(3)检测棉花植株侧茎:将步骤(2)中获得的包含植株主茎的棉花植株子图像,按照主茎位置,划分成两侧:主茎以左为图像左侧,主茎以右为图像右侧;将两侧图像分别进行颜色分割和直线检测得到两侧图像中的初侧茎,将其中与主茎上侧成锐角的初侧茎作为侧茎;(3) Detect the side stems of cotton plants: Divide the sub-images of cotton plants obtained in step (2) containing the main stems into two sides according to the position of the main stems: the left side of the main stem is the left side of the image, and the right side of the main stem is the right side of the image; color segmentation and straight line detection are carried out on the images on both sides to obtain the primary lateral stems in the images on both sides, and the primary lateral stems which form an acute angle with the upper side of the main stem are used as lateral stems;
(4)判断棉花五真叶期:对于步骤(2)中获得的包含植株主茎的棉花植株子图像,以其中侧茎和主茎的交点作为节点,当检测到2个或者2个以上节点的子图像数目占所有定苗后棉花植株子图像数目50%以上时,判断棉田进入五真叶期,否则进行下一天的检测。(4) Judgment of the five true leaf stages of cotton: For the cotton plant sub-image obtained in step (2) containing the main stem of the plant, the intersection of the side stem and the main stem is used as the node, when two or more nodes are detected When the number of sub-images accounts for more than 50% of the number of sub-images of cotton plants after seedling setting, it is judged that the cotton field has entered the five true leaf stage, otherwise, the next day's detection will be carried out.
优选地,所述的棉花五真叶期自动检测方法,其所述步骤(1)定苗时间按照以下步骤判断:每天在相同条件下采集棉田下视图图像,利用分割方法对所述图像进行绿色分割,统计所述图像中绿色像素所占比例即为绿色图像覆盖度;将每天棉田下视图绿色图像覆盖度与前一天棉田下视图绿色图像覆盖度比较,当绿色图像覆盖度降低时即为定苗时间。Preferably, in the method for automatic detection of the five true leaf stages of cotton, the step (1) of the seedling setting time is judged according to the following steps: collect the lower view image of the cotton field under the same conditions every day, and use the segmentation method to perform green segmentation on the image , and counting the proportion of green pixels in the image is the green image coverage; compare the green image coverage of the lower view of the cotton field every day with the green image coverage of the lower view of the cotton field of the previous day, and when the green image coverage decreases, it is the seedling setting time .
优选地,所述的棉花五真叶期自动检测方法,其所述进行绿色分割所使用的分割方法,可采用环境自适应分割方法、超绿算子分割方法、基于Mean shift的作物图像分割方法、Fisher线性判别方法。Preferably, the automatic detection method of the five true leaf stages of cotton, the segmentation method used for the green segmentation, can adopt the environment adaptive segmentation method, the super green operator segmentation method, the crop image segmentation method based on Mean shift , Fisher linear discriminant method.
优选地,所述的棉花五真叶期自动检测方法,其所述单排植株正向前视图经对比度拉伸的方法处理。Preferably, in the automatic detection method of the five true leaf stages of cotton, the front view of the single row of plants is processed by a contrast stretching method.
优选地,所述的棉花五真叶期自动检测方法,其所述搜索框与所述单排植株正向前视图的高相等,所述搜索框的宽度为其高度的1/4至1/2,所述拆分步长为所述搜索框宽度的1/2至5/6。Preferably, in the automatic detection method of the five true leaf stages of cotton, the search box is equal to the height of the front view of the single row of plants, and the width of the search box is 1/4 to 1/3 of its height. 2. The split step is 1/2 to 5/6 of the width of the search box.
优选地,所述的棉花五真叶期自动检测方法,其所述步骤(2)边缘检测算法和骨架检测算法,可采用的图像检测算子有Sobel算子、Roberts算子、LoG算子和Canny算子,优选Canny算子。Preferably, in the automatic detection method of the five true leaf stages of cotton, in the step (2) edge detection algorithm and skeleton detection algorithm, the image detection operators that can be used include Sobel operator, Roberts operator, LoG operator and Canny operator, preferably Canny operator.
优选地,所述的棉花五真叶期自动检测方法,其所述步骤(2)和步骤(3)中直线检测可采用Hough变换。Preferably, in the method for automatic detection of five true leaf stages of cotton, the straight line detection in the step (2) and step (3) can use Hough transform.
按照本发明的另一个方面,提供了一种棉花五真叶期自动检测系统,其特征在于,包括棉花植株子图像获取模块、棉花主茎检测模块、棉花侧茎检测模块以及棉花五真叶期判断模块;According to another aspect of the present invention, an automatic detection system for the five true leaf stages of cotton is provided, which is characterized in that it includes a cotton plant sub-image acquisition module, a cotton main stem detection module, a cotton side stem detection module, and a cotton five true leaf stage Judgment module;
所述棉花植株子图像获取模块,用于采集定苗后棉田单排植株正向前视图,拆分成棉花植株单株子图像,并将所述子图像处理成定苗后棉花植株子图像传递给棉花主茎检测模块;The cotton plant sub-image acquisition module is used to collect the front view of a single row of cotton plants in a cotton field after seedling setting, split them into individual cotton plant sub-images, and process the sub-images into cotton plant sub-images after seedling setting to transfer to cotton main stem detection module;
所述棉花主茎检测模块,用于提取棉花植株边缘和之主骨架,将包含竖直植株骨架的竖直植株边缘内侧作为棉花植株主茎,获得包含植株主茎的棉花植株子图像,并将所述子图像传递给棉花侧茎检测模块;The cotton main stem detection module is used to extract the edge of the cotton plant and its main skeleton, and the inner side of the vertical plant edge containing the vertical plant skeleton is used as the main stem of the cotton plant to obtain a sub-image of the cotton plant including the main stem of the plant, and The sub-image is delivered to the cotton lateral stem detection module;
所述棉花侧茎检测模块,用于将包含植株主茎的棉花植株子图像,按照主茎位置,划分成两侧:主茎以左为图像左侧,主茎以右为图像右侧;获取两侧图像中的初侧茎,将其中与主茎上侧成锐角的直线作为侧茎,并将检测结果传递给棉花五真叶期判断模块;The cotton side stem detection module is used to divide the cotton plant sub-image including the main stem of the plant into two sides according to the position of the main stem: the left side of the main stem is the left side of the image, and the right side of the main stem is the right side of the image; For the initial lateral stems in the images on both sides, the straight line forming an acute angle with the upper side of the main stem is used as the lateral stems, and the detection results are passed to the cotton five-true leaf stage judgment module;
所述棉花五真叶期判断模块,用于根据定苗后棉花植株子图像中侧茎数目的分布情况,判断棉花是否进入五真叶期:对于包含植株主茎的棉花植株子图像,以其中侧茎和主茎的交点作为节点,当检测到2个或者2个以上节点的子图像数目占所有定苗后棉花植株子图像数目50%以上时,判断棉田进入五真叶期。The cotton five-true-leaf stage judging module is used to determine whether cotton has entered the five-true-leaf stage according to the distribution of the number of side stems in the cotton plant sub-image after the seedlings are settled: for the cotton plant sub-image containing the main stem of the plant, the side stem The intersection of the stem and the main stem is used as a node. When the number of sub-images of 2 or more nodes is detected to account for more than 50% of the number of sub-images of cotton plants after seedling setting, it is judged that the cotton field has entered the five true leaf stage.
总体而言,通过本发明所构思的以上技术方案与现有技术相比,由于采用图像处理的方法分析棉田照片,从而判断棉田是否进入五真叶期,能够取得下列有益效果:Generally speaking, compared with the prior art, the above technical scheme conceived by the present invention can obtain the following beneficial effects due to the use of image processing method to analyze the photos of cotton fields, thereby judging whether the cotton fields have entered the five true leaf stage:
(1)代替人工判断棉花五真叶期,节省人力;(1) Instead of manually judging the five true leaf stages of cotton, it saves manpower;
(2)通过图像处理的方法,能实时监控棉田状态,随时报告棉田是否进入五真叶期,从而有利于农业气象观测;(2) Through the method of image processing, the state of the cotton field can be monitored in real time, and whether the cotton field has entered the five true leaf stage can be reported at any time, which is beneficial to agricultural meteorological observation;
(3)通过精确的分割方法来分析图像,通过统计数据来判断五真叶期,较之人工判断,更为准确可靠;(3) Analyzing images through precise segmentation methods, and judging the five true leaf stages through statistical data, which is more accurate and reliable than manual judgment;
(4)通过合理的优化图像处理参数,选择适合的图像处理算法,兼顾棉田图像处理速度和处理效果。(4) By rationally optimizing the image processing parameters and selecting a suitable image processing algorithm, taking into account the cotton field image processing speed and processing effect.
附图说明Description of drawings
图1是本发明提供的棉花五真叶期自动检测方法流程图;Fig. 1 is the automatic detection method flowchart of cotton five true leaf stages provided by the present invention;
图2是棉田正向前视图;Fig. 2 is the front view of the cotton field;
图3是可清晰观察到单排植株长势的棉田正向前视图;Figure 3 is a front view of a cotton field where the growth of a single row of plants can be clearly observed;
图4是获取定苗后棉花植株子图像处理结果图:图4(a)是植株子图像原图,图4(b)是对比度拉伸结果图,图4(c)是颜色分割结果图,图4(d)是Mean shift分割结果图,图4(e)是综合分割结果图;Figure 4 is the result of cotton plant sub-image processing after seedling acquisition: Figure 4(a) is the original image of the plant sub-image, Figure 4(b) is the result of contrast stretching, and Figure 4(c) is the result of color segmentation. 4(d) is the Mean shift segmentation result graph, and Figure 4(e) is the comprehensive segmentation result graph;
图5是棉田下视图;Fig. 5 is the lower view of the cotton field;
图6是对图5进行绿色分割后的结果图;Fig. 6 is the result map after green segmentation of Fig. 5;
图7是棉田下视图覆盖度变化趋势图;Fig. 7 is the change trend chart of the cotton field lower view coverage;
图8是检测棉花植株主茎处理结果图:图8(a)是棉花植株子图像示例图,图8(b)是植株初边缘二值子图,图8(c)是植株初骨架二值子图,图8(d)是主茎检测结果图;Figure 8 is the result of detecting the main stem of cotton plants: Figure 8(a) is an example of a sub-image of a cotton plant, Figure 8(b) is a binary sub-image of the initial edge of the plant, and Figure 8(c) is the binary value of the initial skeleton of the plant Subgraph, Figure 8(d) is the result of the main stem detection;
图9是检测棉花植株侧茎处理结果图:图9(a)是棉花植株子图像左侧图,图9(b)是棉花植株子图像右侧图,图9(c)是侧茎检测结果图;Figure 9 is the result of detecting cotton plant side stems: Figure 9(a) is the left side of the cotton plant sub-image, Figure 9(b) is the right side of the cotton plant sub-image, and Figure 9(c) is the detection result of the side stem picture;
图10是棉花植株子图像节点检测图。Fig. 10 is a node detection diagram of a cotton plant sub-image.
具体实施方式Detailed ways
为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本发明进行进一步详细说明。应当理解,此处所描述的具体实施例仅仅用以解释本发明,并不用于限定本发明。此外,下面所描述的本发明各个实施方式中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合。In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.
本发明提供的棉花五真叶期自动检测方法,包括以下步骤:The automatic detection method of the five true leaf stages of cotton provided by the invention comprises the following steps:
(1)获取定苗后棉花植株子图像。(1) Obtain the sub-image of the cotton plant after seedling setting.
采集定苗后棉田正向前视图,采用离地面高0.35米,镜头焦距为14毫米,水平拍摄方向向东,与地平线夹角0度,相机分辨率不低于400万像素。以每一天为一检测时段,两个相机每一检测时段内分别拍摄w张棉花图像(w=13)。每天为一检测阶段,有利于识别棉花的主要关键生长期。由于我们图像处理的对象为单株棉花植株,因此首先要对定苗后的棉田正向前视图进行裁切,以获得可清晰观察到单排植株生长状况的棉田正向前视图。由于在我们获取的定苗后棉田正向前视序列图中,单排植株分布于整张图的下部1/3范围内,因此图像的裁切大小优选为“棉田正向前视图的宽”×(1/3ד正向前视图像的高”)。裁切后,获得定苗后棉田单排植株正向前视图。Collect the front view of the cotton field after the seedlings have been settled. The height from the ground is 0.35 meters, the focal length of the lens is 14 mm, the horizontal shooting direction is east, the angle with the horizon is 0 degrees, and the resolution of the camera is not less than 4 million pixels. Taking each day as a detection period, the two cameras took w cotton images (w=13) in each detection period. Every day is a detection period, which is beneficial to identify the main key growth period of cotton. Since the object of our image processing is a single cotton plant, it is first necessary to cut the front view of the cotton field after seedling setting to obtain a front view of the cotton field that can clearly observe the growth status of a single row of plants. Since the single-row plants are distributed in the lower 1/3 of the whole picture in the sequence picture of the front view of the cotton field after seedling setting, the cut size of the image is preferably "the width of the front view of the cotton field" × (1/3 x "height of front view image"). After cutting, obtain the front view of the single row of plants in the cotton field after seedling setting.
用适合棉花植株单株图像大小的搜索框以一定的拆分步长,将所述图像拆分成子图像。优选地,所述搜索框与所述单排植株正向前视图的高相等,所述搜索框的宽度为其高度的1/4至1/2,所述拆分步长为所述搜索框宽度的1/2至5/6。对单株植株的图像进行观察后发现,植株的长宽比一般为3:1,由于子图像的大小影响到检测的完整性和准确性,因此子图像大小可更优选为所述搜索框与所述单排植株正向前视图的高相等,所述搜索框的宽度为其高度的1/3。拆分顺序影响到搜索程序设计,拆分顺序优选从左到右;拆分步长影响到单株植株搜索的时间和精度,拆分步长越大拆分速度越快精度越低,步长越小拆分速度越慢精度越高,拆分步长优选为所述搜索框宽度的1/2至5/6,为了确保检测到单排植株中的每一株及避免重复检测到同一株植株,更优选为所述搜索框宽度的4/5。Using a search box suitable for the image size of a single cotton plant, the image is split into sub-images with a certain split step. Preferably, the search box is equal to the height of the front view of the single row of plants, the width of the search box is 1/4 to 1/2 of its height, and the split step is 1/2 to 5/6 of the width. After observing the image of a single plant, it is found that the aspect ratio of the plant is generally 3:1. Since the size of the sub-image affects the integrity and accuracy of detection, the size of the sub-image can be more preferably the size of the search box and The height of the front view of the single row of plants is equal, and the width of the search box is 1/3 of its height. The split order affects the search program design, and the split order is preferably from left to right; the split step affects the time and accuracy of a single plant search, the larger the split step, the faster the split, the lower the accuracy, and the step size The smaller the splitting speed, the slower the splitting speed and the higher the accuracy, the splitting step is preferably 1/2 to 5/6 of the width of the search box, in order to ensure that each plant in a single row of plants is detected and to avoid repeated detection of the same plant Plants, more preferably 4/5 of the width of the search box.
为了更加精确的进行后续检测,可先利用对比度拉伸的方法,突出显示植株。对拉伸后的图像:采用颜色分割或作物图像分割方法等不同方法获得单株植株分割结果图。棉花植株叶片及主茎上部为绿色,主茎下部为红棕色。基于单株棉花植株的颜色特征,优选的,采用RGB(红/绿/蓝)颜色空间中三个颜色通道之间的关系对子图像进行绿色分割和红棕色分割。对图像进行绿色分割时,采用R、G/R、G/B值作为特征指标,若图像中像素三通道值之间的关系同时满足R≤200,G/R≥0.9和G/B≥2.5,则可基本将绿色叶片分割出来;对图像进行红棕色分割时,采用R、R/G、B/G值作为特征指标,若图像中像素三通道值之间的关系同时满足R>100,R/G≥2.8和B/G≥0.1,则可基本将红棕色枝杈分割出来,结合绿色和红棕色分割结果,可以获得单株植株的分割结果。采用作物图像分割方法,利用空间信息过滤田间自然环境中图像的背景噪声,优选的作物分割方法为基于Meanshift的作物分割方法,获得作物图像分割结果。将作物图像分割结果和颜色分割的结果结合起来,即在基于Mean shift的作物分割方法的区域分割结果图上,保留颜色分割结果图中已检测到的像素所占据的区域,以获得定苗后棉花植株子图像。In order to carry out subsequent detection more accurately, the method of contrast stretching can be used first to highlight the plants. For the stretched image: use different methods such as color segmentation or crop image segmentation to obtain a single plant segmentation result map. Cotton plant leaves and the upper part of the main stem are green, and the lower part of the main stem is reddish brown. Based on the color features of a single cotton plant, preferably, the sub-image is segmented into green and reddish brown using the relationship between the three color channels in the RGB (red/green/blue) color space. When performing green segmentation on an image, R, G/R, and G/B values are used as feature indicators. If the relationship between the three channel values of pixels in the image satisfies R≤200, G/R≥0.9 and G/B≥2.5 at the same time , then the green leaves can be basically segmented out; when reddish-brown image is segmented, R, R/G, B/G values are used as feature indicators, if the relationship between the three channel values of pixels in the image satisfies R>100 at the same time, If R/G≥2.8 and B/G≥0.1, the reddish-brown branches can be basically segmented, and the segmentation results of a single plant can be obtained by combining the green and reddish-brown segmentation results. The crop image segmentation method is used to filter the background noise of the image in the field natural environment by using spatial information. The crop segmentation method based on Meanshift is the preferred crop segmentation method to obtain the crop image segmentation result. Combine the results of crop image segmentation and color segmentation, that is, on the area segmentation result map of the crop segmentation method based on Mean shift, retain the area occupied by the detected pixels in the color segmentation result map to obtain the cotton after planting Plant subimage.
由于田间自然环境中图像背景复杂,使用RGB颜色空间中三个颜色通道之间的关系进行颜色分割时,没有利用到空间信息,导致分割结果中会出现噪声以及孔隙。因此,再采用基于Mean shift的作物图像分割方法对子图像进行分割。最后,将Mean shift和颜色分割的结果结合起来,即结合空间和颜色信息,能够更准确的分割出植株,得到较为清晰的棉花植株子图像。Due to the complexity of the image background in the natural environment of the field, when using the relationship between the three color channels in the RGB color space for color segmentation, the spatial information is not utilized, resulting in noise and pores in the segmentation results. Therefore, the crop image segmentation method based on Mean shift is used to segment the sub-image. Finally, combining the results of Mean shift and color segmentation, that is, combining space and color information, can segment plants more accurately and obtain a clearer sub-image of cotton plants.
定苗时间按照如下方法确定:每天在相同条件下采集棉田下视图图像,优选地,采用离地面高5米的相机,镜头焦距为14毫米,水平拍摄方向向东,与地平线夹角向下60度,相机分辨率不低于400万像素。利用分割方法对所述图像进行绿色分割,统计所述图像中绿色像素所占比例即为绿色图像覆盖度;将每天棉田下视图绿色图像覆盖度与前一天棉田下视图绿色图像覆盖度比较,当绿色图像覆盖度降低时即为定苗时间。所述分割方法可采用环境自适应分割方法、超绿算子分割方法、基于Mean shift的作物图像分割方法、Fisher线性判别方法等方法(参见[1]Lei F.Tian.Environmentally adaptive segmentation algorithm for outdoor imagesegmentation.Computers and electronics in agriculture,1998,21:153~168);[2]D.M.Woebbecke,G.E.Meyer,K.Von Bargen,D.A.Mortensen.ColorIndices for weed identification under various soil,residue,and lightingconditions.Transactions of the ASAE,1995,38(1):259~269);[3]Zheng L,Zhang J,Wang Q.Mean-shift-based color segmentation of images containinggreen vegetation.Computers and Electronics in Agriculture,2009,65:93-98);[4]Zheng L,Shi D,Zhang J.Segmentation of green vegetation of crop canopyimages based on mean shift and Fisher linear discriminant.Pattern RecognitionLetters,2010,31(9):920~925.)。The time for setting seedlings is determined as follows: collect the lower view images of the cotton field under the same conditions every day, preferably, a camera 5 meters above the ground is used, the focal length of the lens is 14 mm, the horizontal shooting direction is east, and the angle with the horizon is 60 degrees downward , the camera resolution is not less than 4 million pixels. Utilize segmentation method to carry out green segmentation to described image, count the proportion of green pixel in described image and be green image coverage; The green image coverage of the lower view of cotton field every day is compared with the green image coverage of cotton field lower view of the previous day, when When the coverage of the green image decreases, it is the seedling setting time. Described segmentation method can adopt methods such as environmental adaptive segmentation method, hypergreen operator segmentation method, crop image segmentation method based on Mean shift, Fisher linear discriminant method (referring to [1] Lei F.Tian.Environmentally adaptive segmentation algorithm for outdoor imagesegmentation.Computers and electronics in agriculture,1998,21:153~168); [2]D.M.Woebbecke,G.E.Meyer,K.Von Bargen,D.A.Mortensen.Color Indices for weed identification under various soil,residue,and lighting conditions of the Transactions ASAE,1995,38(1):259~269); [3] Zheng L, Zhang J, Wang Q. Mean-shift-based color segmentation of images containing green vegetation. Computers and Electronics in Agriculture, 2009, 65:93- 98); [4] Zheng L, Shi D, Zhang J. Segmentation of green vegetation of crop canopy images based on mean shift and Fisher linear discriminant. Pattern Recognition Letters, 2010, 31 (9): 920~925.).
(2)检测棉花植株主茎。(2) Detect the main stem of the cotton plant.
对于步骤(1)中获得的所有棉花植株子图像,采用边缘检测算法检测植株初边缘,得到边缘二值子图;采用骨架检测算法提取植株初骨架,得到骨架二值子图。所述边缘检测算法,可采用的图像检测算子有Sobel算子、Roberts算子、LoG算子和Canny算子等,优选边缘完整性较强的Canny算子进行边缘检测。所述骨架检测算法,优选形态学图像处理方法中的细化操作。For all the cotton plant sub-images obtained in step (1), the edge detection algorithm is used to detect the initial edge of the plant to obtain the edge binary sub-image; the skeleton detection algorithm is used to extract the initial plant skeleton to obtain the skeleton binary sub-image. For the edge detection algorithm, image detection operators that can be used include Sobel operator, Roberts operator, LoG operator, and Canny operator, etc., and the Canny operator with strong edge integrity is preferred for edge detection. The skeleton detection algorithm is preferably a thinning operation in the morphological image processing method.
对边缘二值子图和骨架二值子图进行链码检测和直线检测,得到棉花植株竖直的植株边缘和植株骨架。将边缘二值子图和骨架二值子图叠加,将包含竖直植株骨架的竖直植株边缘内侧作为棉花植株主茎,获得包含植株主茎的棉花植株子图像。所述直线检测优选采用Hough变换算法。Chain code detection and straight line detection are performed on the edge binary subgraph and the skeleton binary subgraph, and the vertical plant edge and plant skeleton of the cotton plant are obtained. The edge binary submap and the skeleton binary submap are superimposed, and the inner side of the vertical plant edge containing the vertical plant skeleton is used as the main stem of the cotton plant to obtain the subimage of the cotton plant including the main stem of the plant. The straight line detection preferably adopts the Hough transform algorithm.
棉花植株的主茎具有明显的直线边缘,主茎生长方向也基本为垂直方向,因此可以采用检测垂直方向直线的方式来判断植株边缘提取子图像中是否有主茎存在。将植株算子边缘提取二值子图和植株骨架线边缘提取二值子图,都分别先采用链码提取直线,然后采用Hough变换对链码提取直线的结果进行直线检测,最后将算子边缘提取二值子图的检测直线结果与骨架线边缘提取二值子图的检测直线结果合并,可以得到最终的主茎检测结果。The main stem of the cotton plant has obvious straight-line edges, and the growth direction of the main stem is basically vertical. Therefore, the method of detecting the vertical line can be used to judge whether there is a main stem in the plant edge extraction sub-image. Extract the binary subgraph of the edge of the plant operator and the binary subgraph of the edge of the plant skeleton line, respectively first use the chain code to extract the straight line, and then use the Hough transform to detect the straight line result of the chain code extraction line, and finally the operator edge The detection straight line result of extracting the binary subgraph is merged with the detection straight line result of the skeleton line edge extraction binary subgraph, and the final main stem detection result can be obtained.
(3)检测棉花植株侧茎。(3) Detection of lateral stems of cotton plants.
将步骤(2)中获得的包含植株主茎的棉花植株子图像,按照主茎位置,划分成两侧:主茎以左为图像左侧,主茎以右为图像右侧。将两侧图像分别进行颜色分割和直线检测得到两侧图像中的初侧茎。The cotton plant sub-image obtained in step (2) including the main stem of the plant is divided into two sides according to the position of the main stem: the left side of the main stem is the left side of the image, and the right side of the main stem is the right side of the image. Color segmentation and straight line detection are performed on the images on both sides to obtain the primary lateral stems in the images on both sides.
颜色分割的原理和步骤如下:棉花植株生长到五真叶期时,单株植株的侧茎颜色会变成红棕色,在两侧图像内根据侧茎的颜色特性,首先利用图像中像素三通道值之间的关系,对基于主茎的单株植株子图像进行颜色分割:采用R、R/G、B/G值作为特征指标,若图像中像素三通道值之间的关系同时满足R>100,R/G≥2.8和B/G≥0.1,则可基本将红棕色侧茎分割出来。The principle and steps of color segmentation are as follows: when a cotton plant grows to the five-true leaf stage, the color of the side stem of a single plant will turn reddish-brown. According to the color characteristics of the side stem in the images on both sides, first use the three-channel pixel in the image to The relationship between the values, color segmentation of the sub-image of a single plant based on the main stem: using R, R/G, B/G values as feature indicators, if the relationship between the pixel three-channel values in the image satisfies R> 100, R/G≥2.8 and B/G≥0.1, the reddish-brown side stems can be basically separated.
分别在两侧图像对完成颜色分割后的基于主茎的单株植株子图像结果图进行直线检测,优选的,采用Hough变换的直线检测方法。Line detection is carried out on the images on both sides of the sub-image of a single plant based on the main stem after the color segmentation is completed. Preferably, a line detection method using Hough transform is used.
两侧图像的初侧茎中,与主茎上侧成锐角的直线被认为是侧茎。Among the primary lateral stems of the images on both sides, the straight line forming an acute angle with the upper side of the main stem was considered as the lateral stem.
(4)判断棉花五真叶期。(4) Determine the five true leaf stage of cotton.
对于步骤(2)中获得的包含植株主茎的棉花植株子图像,以其中侧茎和主茎的交点作为节点,当检测到2个或者2个以上节点的子图像数目占所有定苗后棉花植株子图像数目50%以上时,认为棉田进入五真叶期,否则进行下一天的检测。For the sub-image of the cotton plant containing the main stem of the plant obtained in step (2), the intersection point of the side stem and the main stem is used as the node. When two or more nodes are detected, the number of sub-images accounts for all cotton plants When the number of sub-images is more than 50%, it is considered that the cotton field has entered the five true leaf stage, otherwise the next day's detection will be carried out.
为程序设计方便,采用节点数代表侧茎数目,单株植株上的节点即主茎和侧茎的交点,当颜色分割和直线检测结果判定的主茎和侧茎没有交点时,延长侧茎直至其与主茎相交,确定交点即为节点。经验判断,图像一般无法采集全部节点,因此当检测到2个或者2个以上节点的子图像数目占所有子图像数目50%以上时,判断棉田进入五真叶期,否则判断棉花未进入五真叶期。For the convenience of program design, the number of nodes is used to represent the number of side stems. The nodes on a single plant are the intersection points of the main stem and side stems. It intersects with the main stem, and the point of intersection is determined as a node. Judging by experience, the image generally cannot collect all nodes, so when the number of sub-images with 2 or more nodes is detected to account for more than 50% of all sub-images, it is judged that the cotton field has entered the five-true leaf stage, otherwise it is judged that the cotton has not entered the five-true stage leaf stage.
按照所述棉花五真叶期自动检测方法,提供了一种棉花五真叶期自动检测系统,其特征在于,包括棉花植株子图像获取模块、棉花主茎检测模块、棉花侧茎检测模块以及棉花五真叶期判断模块;According to the automatic detection method for the five true leaf stages of cotton, an automatic detection system for the five true leaf stages of cotton is provided, which is characterized in that it includes a cotton plant sub-image acquisition module, a cotton main stem detection module, a cotton side stem detection module, and a cotton side stem detection module. Five true leaf stage judgment module;
所述棉花植株子图像获取模块,用于采集定苗后棉田单排植株正向前视图,拆分成棉花植株单株子图像,并将所述子图像处理成定苗后棉花植株子图像传递给棉花主茎检测模块;The cotton plant sub-image acquisition module is used to collect the front view of a single row of cotton plants in a cotton field after seedling setting, split them into individual cotton plant sub-images, and process the sub-images into cotton plant sub-images after seedling setting to transfer to cotton main stem detection module;
所述棉花主茎检测模块,用于提取棉花植株边缘和之主骨架,将包含竖直植株骨架的竖直植株边缘内侧作为棉花植株主茎,获得包含植株主茎的棉花植株子图像,并将所述子图像传递给棉花侧茎检测模块;The cotton main stem detection module is used to extract the edge of the cotton plant and its main skeleton, and the inner side of the vertical plant edge containing the vertical plant skeleton is used as the main stem of the cotton plant to obtain a sub-image of the cotton plant including the main stem of the plant, and The sub-image is delivered to the cotton lateral stem detection module;
所述棉花侧茎检测模块,用于将包含植株主茎的棉花植株子图像,按照主茎位置,划分成两侧:主茎以左为图像左侧,主茎以右为图像右侧;获取两侧图像中的初侧茎,将其中与主茎上侧成锐角的直线作为侧茎,并将检测结果传递给棉花五真叶期判断模块;The cotton side stem detection module is used to divide the cotton plant sub-image including the main stem of the plant into two sides according to the position of the main stem: the left side of the main stem is the left side of the image, and the right side of the main stem is the right side of the image; For the initial lateral stems in the images on both sides, the straight line forming an acute angle with the upper side of the main stem is used as the lateral stems, and the detection results are passed to the cotton five-true leaf stage judgment module;
所述棉花五真叶期判断模块,用于根据定苗后棉花植株子图像中侧茎数目的分布情况,判断棉花是否进入五真叶期:对于包含植株主茎的棉花植株子图像,以其中侧茎和主茎的交点作为节点,当检测到2个或者2个以上节点的子图像数目占所有定苗后棉花植株子图像数目50%以上时,判断棉花进入五真叶期,否则判断棉花未进入五真叶期。The cotton five-true-leaf stage judging module is used to determine whether cotton has entered the five-true-leaf stage according to the distribution of the number of side stems in the cotton plant sub-image after the seedlings are settled: for the cotton plant sub-image containing the main stem of the plant, the side stem The intersection of the stem and the main stem is used as a node. When the number of sub-images of 2 or more nodes is detected to account for more than 50% of the number of sub-images of cotton plants after seedling setting, it is judged that the cotton has entered the five true leaf stage, otherwise it is judged that the cotton has not entered Five true leaves stage.
以下为实施例:The following are examples:
使用本发明提供的方法判断图2中的棉花是否进入五真叶期:Use the method provided by the invention to judge whether the cotton in Fig. 2 enters the five true leaf stage:
(1)获取定苗后棉花植株子图像。(1) Obtain the sub-image of the cotton plant after seedling setting.
采集定苗后棉田正向前视图,相机离地面高0.35米,焦距14毫米,水平拍摄方向向东,与地平线夹角为0度,分辨率400万像素,实施例以每一天为一检测时段,相机每一检测时段内分别拍摄w张棉花图像(w=13),图像大小为3648×2736。如图2所示为棉田正向前视图示例图。首先对定苗后的棉田正向前视图进行裁切,以获得可清晰观察到单排植株生长状况的定苗后棉田单排植株正向前视图,图像大小为3648×912,如图3所示。Collect the front view of the cotton field after the seedlings are settled, the camera is 0.35 meters above the ground, the focal length is 14 mm, the horizontal shooting direction is east, the angle with the horizon is 0 degrees, and the resolution is 4 million pixels. The embodiment uses each day as a detection period, The camera takes w cotton images (w=13) in each detection period, and the image size is 3648×2736. Figure 2 is an example of the front view of the cotton field. First, cut the front view of the cotton field after seedling setting to obtain the front view of the single row of plants in the cotton field after seedling setting, which can clearly observe the growth status of the single row of plants. The image size is 3648×912, as shown in Figure 3.
用912像素×304像素的搜索框以240像素的拆分步长,按照从左到右的顺序,将所述图像拆分成912像素×304像素的仅包含单株植株子图像,如图4(a)所示。Using a search box of 912 pixels × 304 pixels and a split step of 240 pixels, in order from left to right, the image is split into sub-images of 912 pixels × 304 pixels that only contain a single plant, as shown in Figure 4 (a) shown.
对于每一个子图像,利用对比度拉伸的方法,突出显示植株,如图4(b)所示。分别在对比度拉伸后的子图像上进行颜色分割和作物分割操作。颜色分割:采用R、G/R、G/B值作为特征指标,若图像中像素三通道值之间的关系同时满足R≤200,G/R≥0.9和G/B≥2.5,则可基本将绿色叶片分割出来;对图像进行红棕色分割时,采用R、R/G、B/G值作为特征指标,若图像中像素三通道值之间的关系同时满足R>100,R/G≥2.8和B/G≥0.1,则可基本将红棕色枝杈分割出来,结合绿色和红棕色分割结果,获得颜色分割结果图,如图4(c)所示。作物分割:采用基于Mean shift的作物分割算法,将图像进行作物分割,结果如图4(d)所示。将作物图像分割结果和颜色分割的结果结合起来,即在基于Mean shift的作物分割方法的区域分割结果图上,保留颜色分割结果图中已检测到的像素所占据的区域,以获得定苗后棉花植株子图像,如图4(e)所示。For each sub-image, the plants are highlighted using the method of contrast stretching, as shown in Fig. 4(b). Color segmentation and crop segmentation operations are performed on the contrast-stretched sub-images, respectively. Color segmentation: R, G/R, and G/B values are used as feature indicators. If the relationship between the three channel values of pixels in the image satisfies R≤200, G/R≥0.9 and G/B≥2.5 at the same time, it can basically Segment the green leaves; when segmenting the reddish-brown image, use the R, R/G, and B/G values as feature indicators. If the relationship between the three channel values of the pixel in the image satisfies R>100 at the same time, R/G≥ 2.8 and B/G≥0.1, the reddish-brown branches can be basically segmented out, and the color segmentation results can be obtained by combining the green and reddish-brown segmentation results, as shown in Figure 4(c). Crop segmentation: The crop segmentation algorithm based on Mean shift is used to segment the image into crops, and the result is shown in Figure 4(d). Combine the results of crop image segmentation and color segmentation, that is, on the area segmentation result map of the crop segmentation method based on Mean shift, retain the area occupied by the detected pixels in the color segmentation result map to obtain the cotton after planting The plant sub-image is shown in Figure 4(e).
定苗时间按照如下方法确定:每天在相同条件下采集棉田下视图图像,相机离地面高5米,焦距14毫米,水平拍摄方向向东,与地平线夹角向下60度,分辨率400万像素,实施例以每一天为一检测时段,相机每一检测时段内分别拍摄w张棉花图像(w=13),图像大小为3648×2736,如图5所示为棉田下视图示例图。利用Fisher线性判别方法对所述图像进行绿色分割,如图6所示为绿色分割结果图,统计所述图像中绿色图像覆盖度;将棉田三真叶期之后每天的棉田下视图绿色图像覆盖度与前一天棉田下视图绿色图像覆盖度比较,当绿色图像覆盖度降低时即为定苗时间,如图7所示为棉田下视图覆盖度变化趋势图,当检测到棉花三真叶期后第9天时覆盖度降低,这说明在这一天棉农完成了棉田定苗操作,即可以从第10天开始对棉田横向前视图像序列进行单株植株生长状况的检测。The seedling setting time is determined according to the following method: Under the same conditions, the cotton field is collected under the same conditions every day, the camera is 5 meters above the ground, the focal length is 14 mm, the horizontal shooting direction is east, the angle with the horizon is 60 degrees downward, and the resolution is 4 million pixels. In the embodiment, each day is regarded as a detection period, and the camera takes w cotton images (w=13) in each detection period, and the image size is 3648×2736, as shown in Figure 5, which is an example of the bottom view of the cotton field. Utilize Fisher's linear discriminant method to carry out green segmentation to described image, as shown in Figure 6 is the green segmentation result map, the green image coverage in the described image of statistics; Cotton field lower view green image coverage of every day after cotton field three true leaf stages Compared with the green image coverage of the lower view of the cotton field the day before, when the coverage of the green image decreases, it is the time for setting seedlings. Figure 7 shows the change trend of the coverage of the lower view of the cotton field. The decrease of coverage at day time indicates that the cotton farmers have completed the cotton field seedling operation on this day, that is, the cotton field transverse front-view image sequence can be used to detect the growth status of a single plant from the 10th day.
(2)检测棉花植株主茎(2) Detection of the main stem of cotton plants
对于步骤(1)中获得的所有棉花植株子图像,如图8(a)所示为一棉花植株子图像示例图,使用Canny算子检测植株初边缘,得到植株初边缘二值子图,如图8(b)所示;采用形态学图像处理方法中的细化操作提取植株初骨架,得到植株初骨架二值子图,如图8(c)所示。For all the sub-images of cotton plants obtained in step (1), as shown in Figure 8(a) is an example of a sub-image of cotton plants, use the Canny operator to detect the initial edge of the plant, and obtain the binary sub-image of the initial edge of the plant, as shown in As shown in Figure 8(b); the initial skeleton of the plant is extracted using the thinning operation in the morphological image processing method, and the binary subgraph of the initial skeleton of the plant is obtained, as shown in Figure 8(c).
将植株初边缘二值子图和植株初骨架二值子图,都分别先采用链码提取直线,然后采用Hough变换对链码提取直线的结果进行直线检测,得到棉花植株竖直的植株边缘和植株骨架的二值子图。将植株边缘二值子图和植株骨架二值子图叠加,将包含竖直植株骨架的竖直植株边缘内侧作为棉花植株主茎,获得包含植株主茎的棉花植株子图像,如图8(d)所示。The binary subgraph of the initial edge of the plant and the binary subgraph of the initial skeleton of the plant are firstly extracted by chain code, and then the result of the straight line extracted by the chain code is detected by Hough transform to obtain the vertical plant edge and A binary subgraph of the plant skeleton. The plant edge binary submap and the plant skeleton binary submap are superimposed, and the inner side of the vertical plant edge containing the vertical plant skeleton is used as the main stem of the cotton plant to obtain the subimage of the cotton plant including the main stem of the plant, as shown in Figure 8(d ) shown.
(3)检测棉花植株侧茎(3) Detection of lateral stems of cotton plants
将步骤(2)中获得的包含植株主茎的棉花植株子图像,按照主茎位置,划分成两侧:主茎以左为图像左侧,如图9(a)所示,主茎以右为图像右侧,如图9(b)所示。将两侧图像分别进行颜色分割:将两侧图像分别进行颜色分割得到基本分割出来的红棕色侧茎,采用R、R/G、B/G值作为特征指标,若图像中像素三通道值之间的关系同时满足R>100,R/G≥2.8和B/G≥0.1,则可基本将红棕色侧茎分割出来,得到基本分割出来的红棕色侧茎,再对颜色分割结果图进行Hough直线检测得到两侧图像中的直线。The cotton plant sub-image obtained in step (2) including the main stem of the plant is divided into two sides according to the position of the main stem: the left side of the main stem is the left side of the image, as shown in Figure 9(a), and the right side of the main stem is is the right side of the image, as shown in Figure 9(b). Color-segment the images on both sides: color-segment the images on both sides to obtain the reddish-brown side stems that are basically segmented, and use R, R/G, and B/G values as feature indicators. If the relationship among them satisfies R>100, R/G≥2.8 and B/G≥0.1 at the same time, then the reddish-brown lateral stem can be basically segmented to obtain the basically segmented reddish-brown lateral stem, and then Hough is performed on the color segmentation result map Straight line detection gets the straight lines in the images on both sides.
两侧图像中,与主茎上侧成锐角的直线被认为是侧茎,将两侧图像合并,可以得到侧茎检测结果,如图9(c)所示。In the images on both sides, the straight line forming an acute angle with the upper side of the main stem is considered as a side stem, and the side stem detection result can be obtained by combining the images on both sides, as shown in Figure 9(c).
(4)判断棉花五真叶期(4) Judging the five true leaf stage of cotton
单株植株上的节点即主茎和侧茎的交点,当颜色分割和直线检测结果判定的主茎和侧茎没有交点时,延长侧茎直至其与主茎相交,确定交点即为节点。经验判断,图像一般无法采集全部节点,对于步骤(2)中获得的包含植株主茎的棉花植株子图像,当检测到2个或者2个以上节点的子图像数目占所有定苗后棉花植株子图像数目50%以上时,认为棉田进入五真叶期。即检测到2个或2个以上侧茎时的子图像,如图10所示。The node on a single plant is the intersection of the main stem and the side stem. When there is no intersection between the main stem and the side stem as determined by the color segmentation and line detection results, the side stem is extended until it intersects with the main stem, and the intersection point is determined to be the node. Judging by experience, the image generally cannot collect all nodes. For the sub-image of cotton plants containing the main stem of the plant obtained in step (2), when the number of sub-images with 2 or more nodes is detected accounts for all the sub-images of cotton plants after seedling setting. When the number is more than 50%, it is considered that the cotton field has entered the five true leaf stage. That is, the sub-image when two or more lateral stems are detected, as shown in Figure 10.
本领域的技术人员容易理解,以上所述仅为本发明的较佳实施例而已,并不用以限制本发明,凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等,均应包含在本发明的保护范围之内。It is easy for those skilled in the art to understand that the above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention, All should be included within the protection scope of the present invention.
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