An imagej-based method for precise measurement of leaf disease area

Abstract

The application belongs to the technical field of leaf disease area measurement, and provides a leaf disease area accurate measurement method based on ImageJ, which uses ImageJ software to replace the traditional method to accurately measure the area of rubber leaf powdery mildew. The calculation effect of the application is evaluated by using the ImageJ method standardization operation. The results show that the method provided by the application can realize the rapid and accurate measurement of the leaf disease area, reduce the measurement cost and operation threshold, can quickly determine the disease index of powdery mildew, and meet the needs of basic agricultural production and scientific research.

Description

Technical Field

[0001] This invention belongs to the field of leaf disease area measurement technology, and relates to the measurement of powdery mildew area on rubber leaves, specifically to a precise measurement method for leaf disease area based on ImageJ. Background Technology

[0002] Leaf diseases are a significant factor affecting crop yield and quality. Accurately measuring the area of ​​leaf diseases is a crucial prerequisite for assessing disease severity, developing control strategies, and conducting research on crop disease resistance. Currently, methods for measuring leaf disease area are mainly divided into two categories: traditional manual measurement methods and image measurement methods.

[0003] Traditional manual measurement methods include the graph paper method and the weighing method. The graph paper method requires laying the leaf flat on graph paper and manually counting the number of squares covered by the diseased area to calculate the diseased area. This method is cumbersome, inefficient, and easily affected by human counting errors, resulting in poor accuracy. The weighing method requires cutting off the diseased portion of the leaf, weighing it, and then converting the weight per unit area of ​​leaf to calculate the diseased area. This method causes destructive damage to the leaf, cannot achieve dynamic monitoring of the same leaf, and is not suitable for thin or fragile leaves. Currently, most investigations of powdery mildew on rubber leaves follow these steps:

[0004] 1. Leaf sampling: From each group of standard plants, 20 leaf clusters were randomly cut, and 5 leaflets were picked from each leaf cluster, for a total of 100 leaflets as samples.

[0005] 2. Disease diagnosis: Observe the leaflets with a magnifying glass. If white powdery substances (fossilized spores) or corresponding lesions appear on the leaf surface, the leaf is considered to be diseased.

[0006] 3. Calculation of disease incidence: Total disease incidence (%) = "(Number of trees in the bronze leaf stage + number of trees in the light green leaf stage out of 100 trees) × (Number of diseased leaves out of 100 leaflets)" ÷ 10000.

[0007] Classification based on the proportion of diseased area to the total leaf area facilitates the calculation of the disease index:

[0008] Grade 0: No lesions;

[0009] Grade 1: Lesion area < 5%;

[0010] Grade 2: 5% ≤ lesion area < 20%;

[0011] Grade 3: 20% ≤ lesion area < 50%;

[0012] Grade 4: Lesion area ≥ 50%.

[0013] Disease index = Σ (number of diseased leaves at each level × corresponding level) ÷ (total number of leaves surveyed × highest level) × 100.

[0014] ImageJ is an open-source image processing and analysis software with powerful image editing and measurement capabilities. It is free and easy to use, and has been widely applied in various fields. However, there is currently no standardized and accurate method for measuring leaf disease area based on ImageJ that can effectively solve key problems such as image preprocessing, accurate segmentation of diseased areas, and measurement error control. Therefore, this invention provides an accurate method for measuring leaf disease area based on ImageJ to improve the efficiency of powdery mildew investigation on rubber leaves. Summary of the Invention

[0015] The purpose of this invention is to provide a precise measurement method for leaf disease area based on ImageJ, providing a new approach and means for calculating the area of ​​powdery mildew on rubber leaves.

[0016] To achieve the above objectives, the technical solution of the present invention is as follows:

[0017] This invention provides a method for measuring leaf disease area based on ImageJ, the method specifically including:

[0018] (1) Image acquisition: The rubber blade is placed on a white background and flattened. A precision ruler is placed parallel to the blade. The distance of the light source is fixed in the dark room. A high-definition digital camera is used to take pictures perpendicular to the blade plane at a shooting distance of 30 cm to obtain a color image of the blade.

[0019] (2) Analyze \ Set Scale: Set the scale according to the accuracy of the ruler to complete the scale calibration;

[0020] (3) Image enhancement: Image>Adjust>Color Balance, Image>Adjust>Brightness / Contrast, Process>Filters>Unsharp Mask, adjust color balance and contrast / brightness, set Filters>Unsharp Mask to manually correct areas of the image selection that are incorrect;

[0021] (4) Image / Color / Colour Deconvolution / Masson Trichrome: Separate lesions and leaf areas: Click OK to see the separation results under the red, blue and green color channels. Select the color channel with the clearest contrast.

[0022] (5) Image / Type / 8bit: Converts the image to grayscale mode;

[0023] (6) Image>Adjust>Threshold: Adjust the adaptive threshold to obtain preliminary disease area segmentation results;

[0024] (7) Analyze / Measure: Calculate the incidence of powdery mildew and export the measurement data in an editable format.

[0025] Preferably, the manual correction is: using a brush tool to supplement the selected edge disease areas that were missed, and deleting the mistakenly selected fluff or background impurities.

[0026] Preferably, the parameter adjustment requirements for image enhancement are as follows: by adjusting specific parameters, all leaf areas and diseased areas on the rubber leaves are selected so that they are significantly distinguished from the background.

[0027] The present invention also provides the application of the above-described measurement method in determining the area of ​​powdery mildew on rubber leaves.

[0028] The present invention also provides the application of the above-described measurement method in determining the area of ​​a leaf.

[0029] The present invention also provides the application of the above-described measurement method in determining the disease index of rubber leaves.

[0030] The beneficial effects of this invention are:

[0031] This invention provides a precise measurement method for powdery mildew area of ​​rubber leaves based on ImageJ. Addressing the problems of existing leaf disease area measurement methods, such as cumbersome operation, low efficiency, poor accuracy, high cost, or limited applicability, the measurement method provided by this invention enables rapid, accurate, and non-destructive measurement of leaf disease area, reducing measurement costs and operational barriers, and meeting the needs of grassroots agricultural production and scientific research. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the scale and scale settings in this invention;

[0033] Figure 2 This is a schematic diagram of the brightness and contrast settings in this invention;

[0034] Figure 3 This is a schematic diagram illustrating the separation of lesions and leaf regions in this invention;

[0035] Figure 4 This is a schematic diagram of grayscale mode conversion in this invention;

[0036] Figure 5 This is a schematic diagram of filtering lesions in this invention;

[0037] Figure 6 This is a graph showing the statistical results of the lesion area in rubber leaves in this invention. Detailed Implementation

[0038] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0039] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0041] Example: Method for measuring powdery mildew area on rubber leaves using ImageJ software

[0042] Image acquisition: Place the rubber blade flat on a white background, and gently press the edge of the blade with a transparent pressure plate (to avoid wrinkles). Place a 16 mm precision ruler parallel to the blade, ensuring that the ruler is clearly visible and unobstructed. Choose a cloudy environment, use a high-definition digital camera to take pictures perpendicular to the plane of the blade, keep the shooting distance at 30 cm, keep the light source distance fixed, ensure uniform lighting, avoid shadows, and obtain a color image of the blade.

[0043] Scale calibration: Use the "Straight Line Selection Tool" to draw a straight line along the 16 mm mark on the scale. Click "Analyze > Set Scale", enter 16 in the "Known distance" field and cm in the "unit" field. Click "OK" to complete the scale calibration.

[0044] Image Enhancement: Click "Image - Adjustments - Color Balance" to increase the proportion of the green channel by 25% and decrease the proportions of the red and blue channels by 12.5% ​​each. Then click "Image - Adjustments - Contrast / Brightness" to adjust the contrast to 35 and the brightness to 15 to enhance the difference between powdery mildew areas and healthy areas. Finally, click "Filters - Unsharp Mask" and set the radius to 1.2, the amount to 0.6, and the threshold to 0 to further enhance the clarity of the boundaries and distinguish between leaf hairs and diseased areas. Manual Correction: Observe the segmentation results and focus on distinguishing between leaf hairs and powdery mildew patches. Use the "Brush Tool" (set the brush size to 3 pixels) to fill in the selected but missed edge diseased areas. Delete the misselected hairs or background impurities to complete the accurate segmentation of the diseased areas.

[0045] To separate lesions from leaf areas: Select Image / Color / Colour Deconvolution / MassonTrichrome: Click OK to see the separation results under the red, blue, and green color channels. Select the color channel with the clearest contrast.

[0046] Convert an image to grayscale mode: Convert the color mode of the image using Image / Type / 8bit.

[0047] Adaptive Threshold Segmentation: Click "Image > Adjust > Threshold", select "Auto Threshold" in the pop-up threshold window, set the block size to 70 and the offset to 10, and click "Apply" to obtain the preliminary disease area segmentation results;

[0048] Results Calculation and Output: "Analyze / Measure" calculates the incidence of powdery mildew (powdery mildew incidence = powdery mildew area / total rubber leaf area × 100%), and exports the measurement data in an editable format.

[0049] Application examples

[0050] The operation steps of the embodiment are used to calculate the area of ​​powdery mildew on rubber leaves.

[0051] The operation steps and results are as follows:

[0052] First, convert the image's pixel dimensions to their actual physical size using Analyze\Set Scale. Then, draw a line segment on the ruler using the line tool. In the Set Scale dialog box, set the relevant parameters: Distance inpixels is the pixel size of the image during scanning; Known distance is the length of the drawn line segment; Pixel AspectRatio is the magnification factor (default is 1.0); Unit of Length is the unit of length for the drawn line segment. Selecting "Global" means this setting will apply to all subsequent images. Click OK to complete the ruler setting. Figure 1 ).

[0053] Adjust image brightness and contrast ( Figure 2 For plants with darker leaf color, it is difficult to distinguish lesions. Therefore, before measuring the area of ​​lesions, it is necessary to adjust the brightness and contrast of the image. This can be done through Image / Adjust / Brightness / Contrast.

[0054] Separate lesions and leaf areas ( Figure 3To more accurately distinguish between lesions and leaf areas, the color of the lesions can be separated from the color of the leaves. You can see the separation results in the red, blue, and green color channels through Image / Color / Colour Deconvolution / Masson Trichrome / Ok. Differences in the background of the image will cause different contrasts in different color channels. Select the color channel with the clearest contrast.

[0055] Convert the image to grayscale mode ( Figure 4 ): Convert the color mode of the image using Image / Type / 8bit.

[0056] Adaptive Threshold Segmentation Adjustment: Click "Image > Adjust > Threshold", select "Auto Threshold" in the pop-up threshold window, set the block size to 70 and the offset to 10, and click "Apply" to obtain the preliminary disease area segmentation results;

[0057] Filter the lesions and number them in sequence. Figure 5 Click "Analyze / Measure" to calculate the incidence of powdery mildew. Save the results as an .xls file. Sum the results in Excel to get the total area of ​​the lesions. Figure 6 ).

[0058] Based on the above procedures, the powdery mildew area on the rubber leaves and the total area of ​​the rubber leaves were obtained, and the powdery mildew incidence rate was calculated. The powdery mildew area was 23.241 cm². 2 The total area of ​​the rubber blades is 57.789 cm². 2 The incidence of powdery mildew = powdery mildew area / total rubber leaf area × 100% = 40.22%.

[0059] Ten more rubber tree leaves were selected and calculated using the method described above. The results were compared with those from traditional calculations, and the disease severity was determined. The results are shown in Table 1.

[0060]

[0061] In summary, the Imagea J measurement method provided by this invention can obtain accurate disease area, calculate the disease incidence rate of rubber leaves, and further clarify the disease index. It is faster, more efficient, and more accurate than traditional calculation methods, and can provide more precise information for the prevention and control of rubber leaf diseases.

[0062] The above-described embodiments are merely preferred embodiments of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for measuring leaf disease area based on ImageJ, characterized in that, The method specifically includes: (1) Image acquisition: The rubber blade is placed on a white background and flattened. A precision ruler is placed parallel to the blade. The distance of the light source is fixed in the dark room. A high-definition digital camera is used to take pictures perpendicular to the blade plane at a shooting distance of 30 cm to obtain a color image of the blade. (2) Analyze \ Set Scale: Set the scale according to the accuracy of the ruler to complete the scale calibration; (3) Image enhancement: Image>Adjust>Color Balance, Image>Adjust>Brightness / Contrast, Process>Filters>Unsharp Mask, adjust color balance and contrast / brightness, set Filters>UnsharpMask, manually correct the wrong selection area in the image; (4) Image / Color / Colour Deconvolution / Masson Trichrome: Separate lesions and leaf areas: Click OK to see the separation results under the red, blue and green color channels. Select the color channel with the clearest contrast. (5) Image / Type / 8bit: Converts the image to grayscale mode; (6) Image>Adjust>Threshold: Adjust the adaptive threshold to obtain preliminary disease area segmentation results; (7) Analyze / Measure: Calculate the incidence of powdery mildew and export the measurement data in an editable format.

2. The measurement method according to claim 1, characterized in that, The manual correction involves using the brush tool to supplement the selected edge disease areas that were missed, and deleting the mistakenly selected fluff or background impurities.

3. The measurement method according to claim 1, characterized in that, The specific requirements for adjusting the image enhancement parameters are as follows: by adjusting the specific parameters, select all the leaf area and disease area on the rubber leaf to make it significantly distinguishable from the background.

4. The application of the measurement method according to any one of claims 1-3 in determining the area of ​​powdery mildew on rubber leaves.

5. The application of the measurement method according to any one of claims 1-3 in determining the leaf area.

6. The application of the measurement method according to any one of claims 1-3 in determining the disease index of rubber leaves.

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