CN202267464U - Mobile phone based device for rapidly detecting blade area - Google Patents
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Abstract
本实用新型公开了一种基于手机的快速检测叶片面积的装置,包括有手机(4)、背景板(1)、参照物(2)和被测叶片(3),所述的参照物(2)和被测叶片(3)分别置于背景板(1)上不同位置,所述的手机(4)位于背景板(1)的垂直上方,其具有拍照、存储、图像处理、统计分析、人机交互和显示功能;所述的背景板(1)正面的颜色区别于被测叶片(3)的颜色和参照物(2)的颜色;所述的参照物(2)的颜色区别于被测叶片(3)的颜色。本实用新型的检测装置不仅结构简单,便于携带,而且还简化了测量步骤,缩短了检测时间,提高了测量精度。
The utility model discloses a device for quickly detecting the area of a blade based on a mobile phone, which comprises a mobile phone (4), a background plate (1), a reference object (2) and a blade to be measured (3), and the reference object (2 ) and the tested blade (3) are respectively placed in different positions on the background board (1), and the mobile phone (4) is located vertically above computer interaction and display functions; the color of the front of the background board (1) is different from the color of the measured leaf (3) and the color of the reference object (2); the color of the reference object (2) is different from the color of the measured The color of the blade (3). The detection device of the utility model is not only simple in structure and easy to carry, but also simplifies the measurement steps, shortens the detection time and improves the measurement accuracy.
Description
技术领域 technical field
本实用新型涉及一种基于手机的快速检测叶片面积的装置。 The utility model relates to a device for quickly detecting the blade area based on a mobile phone.
背景技术 Background technique
叶面积是作物栽培和育种实践中常用的指标,是农作物的产量和品质的评价指标,也是理想株型选育、测定害虫危害损失的重要指标,利用该参数可计算作物的用水量、蒸腾作用及产量等,也可分析植物的生长状况,并且建立植物生长模型。叶片是植物进行光合作用合成有机物的重要器官,叶面积的大小在一定程度上直接影响着农作物的产量。植物学研究人员在野外考察时,时常需要获取植物叶片的面积。因此建立方便、准确的叶面积测定方法,对于指导农业生产实践活动,制定高产、优质和高效的栽培技术措施具有积极的意义。 Leaf area is a commonly used index in crop cultivation and breeding practice. It is an evaluation index of crop yield and quality. It is also an important index for ideal plant type breeding and determination of pest damage losses. This parameter can be used to calculate water consumption and transpiration of crops. And yield, etc., can also analyze the growth status of plants, and establish plant growth models. Leaf is an important organ for plants to synthesize organic matter through photosynthesis, and the size of leaf area directly affects the yield of crops to a certain extent. Botanical researchers often need to obtain the area of plant leaves when they are investigating in the field. Therefore, establishing a convenient and accurate method for measuring leaf area has positive significance for guiding agricultural production practices and formulating high-yield, high-quality and efficient cultivation techniques.
目前常用的方法有两大类:一类是破坏性叶片面积测定方法,包括方格法、称重法、像素扫描法等方法,这些方法不能活体测量,将会损坏叶片;第二类是非破坏性叶片面积测定方法,包括回归法、图像处理法和光电法等方法。目前的图像处理法是用各种成像设备将叶片图像采集为数字图像,再传到计算机后用Matlab或自己编程实现面积测量,总的来说,这些都方法比较复杂,过程较为繁琐。 There are two types of commonly used methods at present: one is the destructive leaf area measurement method, including grid method, weighing method, pixel scanning method and other methods, these methods cannot be measured in vivo and will damage the leaf; the second type is non-destructive Methods for measuring the area of leaves, including regression method, image processing method and photoelectric method. The current image processing method is to use various imaging equipment to collect the leaf image into a digital image, and then transfer it to the computer and use Matlab or program to realize the area measurement. Generally speaking, these methods are more complicated and the process is more cumbersome.
发明内容 Contents of the invention
本实用新型旨在克服上述现有存在技术的不足,提供一种基于手机快速检测叶片面积的装置。本实用新型的检测装置不仅结构简单,便于携带,而且还简化了测量步骤,缩短了检测时间,提高了测量精度。 The utility model aims to overcome the shortcomings of the above-mentioned prior art, and provides a device for quickly detecting the blade area based on a mobile phone. The detection device of the utility model is not only simple in structure and easy to carry, but also simplifies the measurement steps, shortens the detection time and improves the measurement accuracy.
一种基于手机的快速叶面积检测装置,包括有手机、背景板、参照物和被测叶片,所述的参照物和被测叶片分别置于背景板上不同位置,所述的手机位于背景板的垂直上方,其具有拍照、存储、图像处理、统计分析、人机交互和显示功能;所述的背景板正面的颜色区别于被测叶片的颜色和参照物的颜色;所述的参照物的颜色区别于被测叶片的颜色。 A mobile phone-based rapid leaf area detection device, including a mobile phone, a background plate, a reference object and a measured leaf, the reference object and the measured leaf are respectively placed at different positions on the background plate, and the mobile phone is located on the background plate vertically above, which has the functions of photographing, storage, image processing, statistical analysis, human-computer interaction and display; the color of the front of the background board is different from the color of the measured blade and the color of the reference object; the color of the reference object The color is distinct from the color of the leaf being tested.
本实用新型基于手机的快速检测叶片面积的装置的工作原理: The working principle of the device for quickly detecting the area of the blade based on the mobile phone of the utility model:
a、选择一块正面与被测叶片颜色相区别的纯色不透明平板作为背景板,背景板的面积大于叶片面积,并便于拍摄取景时成像于背景板区域内; a. Select a solid-color opaque flat plate that is different from the color of the leaf under test as the background plate. The area of the background plate is larger than the area of the leaf, and it is convenient to be imaged in the background plate area when shooting and framing;
b、在背景板正面固定一个面积为SR的参照物,参照物的颜色不同于背景板和被测叶片; b. Fix a reference object with an area of S R on the front of the background plate, and the color of the reference object is different from that of the background plate and the leaf under test;
c、将被测叶片展平铺放在背景板正面,且与参照物的位置临近,通过手机的摄像头进行拍摄,获得在背景板区域内,包含被测叶片和参照物在内的完整的数码照片; c. Lay the leaf under test flat on the front of the background board, and close to the position of the reference object, take a picture through the camera of the mobile phone, and obtain a complete digital image including the leaf under test and the reference object in the area of the background board. photo;
d、对照片进行灰度化、滤波、几何校正、二值化和区域连通标记处理,将照片分割为背景、参照物和被测叶片三个区域,通过遍历照片数据,得到背景板的像素总数,参照物的像素总数和被测叶片的像素总数; d. Perform grayscale, filter, geometric correction, binarization and region connectivity marking on the photo, divide the photo into three regions: background, reference object and measured leaf, and obtain the total number of pixels of the background plate by traversing the photo data , the total number of pixels of the reference object and the total number of pixels of the measured blade;
e、通过得到的参照物的像素总数和被测叶片的像素总数,并由用户给定参照物的面积,最后由手机按照如下公式: e. Through the total number of pixels of the reference object and the total number of pixels of the measured leaf, and the area of the reference object is given by the user, and finally the mobile phone follows the following formula:
自动计算得到被测叶片的面积。 The area of the measured blade is automatically calculated.
其中,识别并统计参照物和被测叶片所占像素总数的具体方法是:对照片进行预处理,包括滤波和几何校正,然后对照片进行灰度化和平滑、图像二值化和连通区域标记。经过以上处理以后,将照片分割为背景板,参照物和被测叶片三个区域。最后遍历照片数据可得到背景板的像素总数,参照物的像素总数和叶片的像素总数。通过用户交互比对后可以得到参照物的像素总数和叶片的像素总数。 Among them, the specific method of identifying and counting the total number of pixels occupied by the reference object and the measured leaf is: preprocessing the photo, including filtering and geometric correction, and then graying and smoothing the photo, image binarization and connected region marking . After the above processing, the photo is divided into three areas: the background plate, the reference object and the tested leaf. Finally, the total number of pixels of the background plate, the total number of pixels of the reference object and the total number of pixels of the leaves can be obtained by traversing the photo data. The total number of pixels of the reference object and the total number of pixels of the leaves can be obtained after user interaction comparison.
上述方法中照片的预处理包括灰度化,灰度化是将彩色图像转换为灰度图像。本方法中照片的灰度化是通过将照片颜色的RGB模型转为HIS模型实现的。消去彩色图像中彩色信息里强度分量的影响。HSI颜色模型和RGB颜色模型之间可以通过非线性变换来相互转换: The preprocessing of photos in the above method includes grayscale, which is to convert a color image into a grayscale image. The grayscale of the photo in this method is realized by converting the RGB model of the photo color into the HIS model. Eliminate the influence of the intensity component in the color information in the color image. The HSI color model and the RGB color model can be converted to each other through nonlinear transformation:
对于灰度化后的灰度图像,f(x,y) 的函数值点坐标为(x,y)的像素点的灰度值。 For the grayscale image after grayscale, the function value point coordinate of f(x,y) is the grayscale value of the pixel point of (x,y) .
上述方法中照片的预处理包括滤波,滤波可以减少和消除照片中的“噪音”,以改善照片质量。本方法中采用线性滤波法。线性滤波的算法如下: The preprocessing of photos in the above method includes filtering, which can reduce and eliminate "noise" in photos to improve photo quality. In this method, a linear filtering method is used. The algorithm of linear filtering is as follows:
(1)从左到右,从上到下顺序遍历灰度图像的每一个像素f(x,y); (1) Traverse each pixel f(x,y) of the grayscale image sequentially from left to right and from top to bottom;
(2)把模板算子的中心与该输入像素f(x,y)重叠,把该像素与其模板进行卷积运算,把运算的结果值作为输出图像的对应像素的灰度值; (2) Overlap the center of the template operator with the input pixel f(x,y) , perform convolution operation on the pixel and its template, and use the result value of the operation as the gray value of the corresponding pixel of the output image;
(3)如果所有像素都处理完毕,则算法结束,否则转向(1)。 (3) If all pixels are processed, the algorithm ends, otherwise turn to (1).
上述方法中照片的二值化采用迭代阈值分割法。照片的二值化处理即选择一个灰度阈值,将图像转换为黑白二值图像,迭代阈值分割法的算法如下: The binarization of photos in the above method adopts iterative threshold segmentation method. The binarization processing of photos is to select a gray threshold and convert the image into a black and white binary image. The algorithm of the iterative threshold segmentation method is as follows:
假设取照片灰度范围的中间值作为初始阈值T 0 ,则它的数学表达式为: Assuming that the middle value of the gray range of the photo is taken as the initial threshold T 0 , its mathematical expression is:
其中,L为灰度级的个数,是灰度值为k的像素点的个数。 Among them, L is the number of gray levels, is the number of pixels with gray value k .
具体的实现算法如下: The specific implementation algorithm is as follows:
(1)求出图像的最大灰度值Zmax和最小灰度值Z min,令初始阈值T 0 =(Z max+Z min)/2; (1) Calculate the maximum gray value Z max and the minimum gray value Z min of the image, and set the initial threshold T 0 =( Z max+ Z min)/2;
(2)根据初始阈值T0将图像分割成为目标和背景,分别求出两者的平均灰度值Z1和Z2; (2) According to the initial threshold T0, the image is divided into target and background, and the average gray value Z 1 and Z 2 of the two are calculated respectively;
(3)求出新阈值T=(Z1+Z2)/2; (3) Calculate the new threshold T = ( Z 1+ Z 2 )/2;
(4)若T0≠T,把T的值赋给T0,转到步骤(2),循环迭代计算直到T0=T时终止,所得T即为最优的阈值。最优阈值确定以后进行二值化处理,变换函数表达式如下: (4) If T0 ≠ T , assign the value of T to T0 , go to step (2), and iteratively calculate until T 0 = T , and the obtained T is the optimal threshold. After the optimal threshold is determined, binarization is performed, and the transformation function expression is as follows:
上述方法中对照片连通区域标记采用邻域像素连通标记法。连通区域标记即将二值化图像中邻近的具有相同灰度值像素点赋予同样的标签号。邻域像素连通标记法的算法步骤如下: In the above method, the neighborhood pixel connected labeling method is used for labeling the connected regions of photos. Connected region labeling is to assign the same label number to adjacent pixels with the same gray value in the binarized image. The algorithm steps of the neighborhood pixel connected labeling method are as follows:
(1)从左到右、从上到下扫描照片。对于每行的各点,如果某像素点的灰度值为255,则有以下几种情况:如果上面点和左面点有一个标记,则复制该标记。如果两点有相同的标记,则复制该标记。如果两点有不同的标记,则复制两点中较小的标记,将两标记写入等价表中作为等价标记;否则给这个像素点分配一个新标记,并将这一标记写入等价表。 (1) Scan the photo from left to right and top to bottom. For each point of each row, if the gray value of a certain pixel is 255, there are the following situations: If there is a mark on the upper point and the left point, then copy the mark. If two points have the same marker, that marker is copied. If the two points have different marks, copy the smaller mark of the two points, and write the two marks into the equivalence table as the equivalence mark; otherwise assign a new mark to this pixel, and write this mark into the equivalence table. price list.
(2)考虑下一行,重复第(2)步。 (2) Considering the next row, repeat step (2).
(3)从上到下扫描图像,重复(2)、(3)步。 (3) Scan the image from top to bottom and repeat steps (2) and (3).
(4)在等价表的每一等价集中,找到该等价集中最低的标记。 (4) In each equivalence set of the equivalence table, find the lowest mark in the equivalence set.
(5)遍历图像,用等价表中的最低标记取代每一标记,用不同的颜色标记各个连通区域。 (5) Traverse the image, replace each mark with the lowest mark in the equivalence table, and mark each connected region with a different color.
上述方法中对照片连通区域标记后,遍历照片数据,得到背景板的像素总数,参照物的像素总数和被测叶片的像素总数。通过用户交互比对后可以得到参照物的像素总数和被测叶片的像素总数。通过如下公式计算出被测叶片的面积 In the above method, after the connected regions of the photos are marked, the photo data is traversed to obtain the total number of pixels of the background plate, the total number of pixels of the reference object and the total number of pixels of the measured leaves. The total number of pixels of the reference object and the total number of pixels of the measured leaf can be obtained after user interaction comparison. Calculate the area of the measured blade by the following formula
。 .
本实用新型的叶片面积快速检测方法主要是利用了现有手机的硬件平台和软件平台以及数字图像处理技术,通过软件调用手机的摄像头获取背景板区域内的包含参照物和被测叶片的完整照片,进而通过用Java语言开发的手机上的软件对照片进行图像处理,统计出参照物和被测叶片在该数码照片中所占的像素总数,最后根据公式计算得到被测叶片的面积。 The rapid detection method of the blade area of the utility model mainly utilizes the hardware platform and software platform of the existing mobile phone and the digital image processing technology, and obtains the complete photos including the reference object and the blade under test in the background plate area by calling the camera of the mobile phone through the software , and then carry out image processing on the photo through the software on the mobile phone developed in Java language, count the total number of pixels occupied by the reference object and the measured blade in the digital photo, and finally calculate the area of the measured blade according to the formula.
本实用新型的检测装置不仅结构简单,便于携带,而且还简化了测量步骤,缩短了检测时间,提高了测量精度。 The detection device of the utility model is not only simple in structure and easy to carry, but also simplifies the measurement steps, shortens the detection time and improves the measurement accuracy.
附图说明 Description of drawings
图1是本实用新型基于手机快速检测叶片面积的装置的结构示意图。 Fig. 1 is a structural schematic diagram of a device for rapidly detecting blade area based on a mobile phone of the present invention.
具体实施方式 Detailed ways
下面以实施例并结合附图对本实用新型进行详细的描述,进一步说明本实用新型的目的和特点,但本实用新型的实施方式不局限于此。 The utility model is described in detail below with examples and in conjunction with the accompanying drawings, further illustrating the purpose and characteristics of the utility model, but the implementation of the utility model is not limited thereto.
实施例1 Example 1
如图1所示,本实用新型的一种基于手机快速检测叶片面积的装置,包括有手机4、背景板1、参照物2和被测叶片3,所述的参照物2和被测叶片3分别置于背景板1上不同位置,所述的手机4位于背景板1的垂直上方;所述的手机4具有拍照、存储、图像处理、统计分析、人机交互和显示功能;所述的背景板1正面的颜色区别于被测叶片3的颜色和参照物2的颜色;所述的参照物2的颜色区别于被测叶片3的颜色。 As shown in Fig. 1, a kind of device of the utility model based on mobile phone quickly detects blade area, comprises mobile phone 4, background board 1, reference object 2 and measured blade 3, described reference object 2 and measured blade 3 Placed in different positions on the background board 1 respectively, the mobile phone 4 is positioned at the vertical top of the background board 1; the mobile phone 4 has the functions of photographing, storage, image processing, statistical analysis, human-computer interaction and display; the background The color of the front of the plate 1 is different from the color of the tested blade 3 and the color of the reference object 2; the color of the reference object 2 is different from the color of the tested blade 3. the
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CN109191520A (en) * | 2018-09-30 | 2019-01-11 | 湖北工程学院 | A kind of Measurement Approach of Leaf Area and system based on color calibration |
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2011
- 2011-11-01 CN CN2011204246712U patent/CN202267464U/en not_active Expired - Fee Related
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CN102506772A (en) * | 2011-11-01 | 2012-06-20 | 西北农林科技大学 | Method and device for quickly detecting area of leaf blade based on mobile phone |
CN102865823A (en) * | 2012-10-12 | 2013-01-09 | 西安电子科技大学 | Length measuring method based on currency |
CN105043271A (en) * | 2015-08-06 | 2015-11-11 | 宁波市北仑海伯精密机械制造有限公司 | Method and device for length measurement |
CN105043271B (en) * | 2015-08-06 | 2018-09-18 | 宁波市北仑海伯精密机械制造有限公司 | Length measurement method and device |
CN106404070A (en) * | 2016-10-28 | 2017-02-15 | 浙江理工大学 | Android-based automatic printing and dyeing machine fabric parameter detection system |
CN106404070B (en) * | 2016-10-28 | 2019-01-08 | 浙江理工大学 | A kind of dyeing machine fabric parameter automatic checkout system based on android |
CN109191520A (en) * | 2018-09-30 | 2019-01-11 | 湖北工程学院 | A kind of Measurement Approach of Leaf Area and system based on color calibration |
CN109520447A (en) * | 2018-11-29 | 2019-03-26 | 中国科学院南京地理与湖泊研究所 | A method of amendment image treating measures hydrilla verticillata blade area |
CN115050020A (en) * | 2022-04-29 | 2022-09-13 | 安徽大学 | Intelligent visual detection method and system based on improved Mask R-CNN strawberry leaf area |
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