CN202267464U - Mobile phone based device for rapidly detecting blade area - Google Patents

Abstract

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

A device for quickly detecting blade area based on mobile phone

technical field

The utility model relates to a device for quickly detecting the blade area based on a mobile phone.

Background technique

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.

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.

Contents of the invention

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.

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.

The working principle of the device for quickly detecting the area of the blade based on the mobile phone of the utility model:

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. 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. 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. 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. 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:

The area of the measured blade is automatically calculated.

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.

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:

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) .

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) Traverse each pixel f(x,y) of the grayscale image sequentially from left to right and from top to bottom;

(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) If all pixels are processed, the algorithm ends, otherwise turn to (1).

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:

Assuming that the middle value of the gray range of the photo is taken as the initial threshold T ₀ , its mathematical expression is:

Among them, L is the number of gray levels, is the number of pixels with gray value k .

The specific implementation algorithm is as follows:

(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) 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) Calculate the new threshold T = ( Z 1+ Z 2 )/2;

(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:

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) 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) Considering the next row, repeat step (2).

(3) Scan the image from top to bottom and repeat steps (2) and (3).

(4) In each equivalence set of the equivalence table, find the lowest mark in the equivalence set.

(5) Traverse the image, replace each mark with the lowest mark in the equivalence table, and mark each connected region with a different color.

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

。

.

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.

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 of drawings

Fig. 1 is a structural schematic diagram of a device for rapidly detecting blade area based on a mobile phone of the present invention.

Detailed ways

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.

Example 1

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

Claims (1)

1. A mobile phone-based rapid leaf area detection device, characterized in that: it includes 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 measured blade (3) are respectively placed in different positions on the background plate (1); The mobile phone (4) is located vertically above the background board (1), and has the functions of photographing, storage, image processing, statistical analysis, human-computer interaction and display; 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 leaf (3).