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Jun 2018

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Quantification of Thrips Damage Using Ilastik and ImageJ Fiji
用Ilastik及ImageJ Fiji定量蓟马危害   

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Abstract

Quantification of insect damage is an essential measurement for identifying resistance in plants. In screening for host plant resistance against thrips, the total damaged leaf area is used as a criterion to determine resistance levels. Here we present an objective novel method for analyzing thrips damage on leaf disc using the freely available software programs Ilastik and ImageJ. The protocol was developed in order to screen over 40 Capsicum lines for resistance against Frankliniella occidentalis (Western Flower Thrips) and Thrips tabaci (Onion thrips).

Keywords: Insect resistance (昆虫耐药性), Insect damage (昆虫危害), Image analyses (图像分析)

Background

Quantification of insect damage is an essential measurement for identifying resistance in plants. In screening programs for host-plant resistance against thrips, the total damaged leaf area is used as a criterion to determine resistance levels. Thrips damage is characterized by silvery spots that show high contrast with the intact leaf area, but the feeding spots also include darker areas ranging from dark green to brown. These gradual discolorations of the leaf are too subtle to precisely quantify with programs such as Winfolia (http://www.regentinstruments.com/assets/winfolia_software.html) or ImageJ (Rasband, 2011) alone. As a result, thrips damage is commonly scored by individuals that rate the samples. Samples are classified into categories signifying the amount of damage (Mirnezhad et al., 2010; Maharijaya et al., 2011 and 2012), or damage is estimated to the nearest 1 mm2 (Leiss et al., 2009; Mirnezhad et al., 2010; Maharijaya et al., 2011 and 2012). These subjective measurements make comparison between studies/screening programs difficult. Moreover, they are time consuming and thus costly for breeding companies. Here we present an objective high-throughput standardized screening method to measure leaf surface damage caused by thrips using the freely available software programs ImageJ Fiji (Schindelin et al., 2012) and Ilastik (Sommer et al., 2011). Ilastik has a wide range of applications ranging from cell biology (Fabrowski et al., 2013), where it is used to compute the amount of surface flattening of epithelial cells, to biomechanics (Bongiorno et al., 2014), where it is used to identify boundaries of human mesenchymal stem cells. It is an easy-to-use, self-learning image processing program that allows segmentation and classification of two-dimensional surfaces based on labels provided by the user (Sommer et al., 2011). ImageJ is often used to quantify the amount of removed leaf area by chewing herbivores and the total leaf surface of intact leaves (Meyer and Hull-Sanders, 2008; Morrison and Lindell, 2012). However, it is rarely used to quantify feeding damage caused by thrips. Thrips feeding causes rather subtle discolorations on the leaves. ImageJ is limited in quantifying such color differences, for which Ilastik provides a more suitable alternative.

Materials and Reagents

  1. Filtration paper (e.g., filtration paper nr 600, VWR, catalog number: 516-0309 )
  2. Ziploc® like bags (e.g., 18 x 25 cm, 50 µm polyethylene foliezak met druksluiting, Vink Lisse, catalog number: 174718 49 )
  3. Petri Dish diameter 15 cm (e.g., non-treated culture dishes, Corning, catalog number: 430597 )
  4. Parafilm® M (e.g., BRAND®, Parafilm®, VWR, catalog number: 291-1213 )
  5. Glass jar (Figure 1) (e.g., 555 ml Twist-off pot TO82 with Twist-off deksel RTS82 wit BPA NI lid, www.glazenflessenenpotten.nl, GLAZEN RLESSEN EN POTTEN. NL, catalog numbers: V2391WS and VC305 )


    Figure 1. Glass jar for thrips starvation (height 11.5 cm x diameter 7.5 cm)

  6. Synchronized L1/L2 Frankliniella occidentalis (Pergande) or Thrips tabaci (Lindeman) (Thysanoptera)
  7. Capsicum annuum (Solanaceae) plants (any variety)
  8. Agar (e.g., Phyto Agar, Duchefa Biochemie, catalog number: P1003 )
  9. Water
  10. 1.5% liquid agar solution (see Recipes)

Equipment

  1. Climate cabinet set to 25 °C for F. occidentalis or 23 °C for T. tabaci, L16:D8 light regime (e.g., Economic Delux 432 L with TL lightning, Snijders Labs, http://www.snijderslabs.com)
  2. Microwave (Moulinex, model: Micro-chef FM2515Q )
  3. Cork borer, diameter 1.5 cm (e.g., Humboldt Brass Cork Borer Set with Handles, Fisher Scientific, catalog number: 07-865-10B)
    Manufacturer: Humboldt Mfg., catalog number: H-9663 .
  4. Beaker 50 ml (e.g., Griffin beakers, Corning, PYREX®, catalog number: 1000-50 )
  5. Soft paint brush (e.g., van Eyck paint brush set, brush #1)
  6. Plastic tweezers (e.g., Azlon Forceps - Tweezers, Dynalab Corp., Dynalon Labware, catalog number: 516555-0001 , https://www.dynalon.com/PublicStore/)
  7. Epson 10000XL scanner (Epson, model: 10000XL ) or any SLR camera (12 mega pixel) with tripod
  8. Handmade grid with 2 cm spacing (Figure 3)
  9. Black paper (for scanner) or black cloth (for SLR camera)
  10. Laptop with installed software
  11. Precision balance (Sigma-Aldrich, catalog number: Z267074)
    Manufacturer: Sartorius, model: BP 310 S .
  12. Laboratory bottle with cap 500 ml (DWK Life Sciences, Duran, catalog number: 21 801 44 5 )

Software

  1. ImageJ Fiji e.g., version 2.0.0 with Java 1.6.0_24
  2. Ilastik version 1.1.3,
    Note: For successful application of the protocol, it is important to use this exact version. The software can be found online: files.Ilastik.org/1.1/, ‘ilastik-1.1.3-win64.exe’, also available for Linux and OSX.
  3. Epson Scan Utility e.g., version 3.4.9.9
    Note: Only necessary if an Epson scanner is used or equivalent when using another scanner.

Procedure

  1. Thrips preparation
    Collect synchronized L1/L2 larvae in a glass jar lined with 3 pieces of slightly moist filtration paper 24 h prior to the experiments. Place the glass jar in a climate cabinet set at 25 °C for F. occidentalis or 23 °C for T. tabaci, with a L16:D8 light regime. This 24 h period of starvation ensures that thrips will start feeding directly after the no-choice leaf disc assay is started.

  2. No-choice leaf disc assay
    1. Prepare 1.5 % liquid agar solution (see Recipe 1)
    2. Collect leaves from the plants in the greenhouse and transport to the laboratory in closed Ziploc-like plastic bags containing 2 ml of water to keep the leaves fresh.
    3. Heat the agar in a microwave until it is liquefied.
    4. Use a cork borer to punch leaf discs while avoiding the mid-vein. Midveins often have light color patches that might result in inaccurate thrips damage quantification.
    5. Pour part of the agar solution in a beaker and pour a small drop (approximate 2 cm Ø) of liquid agar in the middle of a Petri dish (Figure 2A). Place the leaf disc on the agar with the adaxial side in full contact with the agar. This ensures that the thrips can only feed on the abaxial side (‘underside’) of the leaf. Place the leaf disc on the agar just before the agar becomes solid (gelling temperature between 34-36 °C, Duchefa Biochemie Safety Data Sheet P1003 version 2.0).
    6. Place five thrips on each leaf disc with a small paintbrush (Figure 2B). Make sure that the agar is fully solid before placing the thrips on the leaf disc.
    7. Close the Petri dishes with Parafilm® M and place them in a climate cabinet for 48 h (Figure 2C).
    8. After 48 h, the thrips can be removed with a small paintbrush and digital images of the leaf discs can be acquired. 


      Figure 2. Experimental setup of leaf disc assay. Placing the leaf disc on the agar (A), inoculating with thrips (B) and placing the closed Petri dishes in a tray in the climate cabinet (C).

  3. Acquiring the images
    1. Place the leaf discs with plastic tweezers on a scanner (or on black cloth in the case an SLR camera is used).
      1. For easy processing of the acquired image, all leaf discs should be equally distributed, so that leaf discs can be easily ‘cut out’ using a single macro run in ImageJ Fiji. Equal distribution of the leaf discs can be achieved using a grid (Figure 3).
      2. The total number of objects (leaf discs and labels) placed on the horizontal and vertical axis should be equal (in our case: horizontal axis: 3 leaf disc + 1 label, vertical axis: 4 leaf discs).
      3. When you use the protocol, it is advisable to include leaf discs that have not been exposed to thrips damage (control leaf discs) and analyze these in the same way as the leaf discs exposed to thrips. Ilastik sometimes overestimates the amount of damage, for example, due to the presence of leaf veins. The undamaged leaf disc images can be used to correct for these errors. 


        Figure 3. Placement of leaf discs. A. Example of TIFF image using a scanner set to 1,200 dpi. Horizontal and vertical axis are marked for illustration with white dashed lines in this example. A ruler is included at the right side of the image for calibration. B. Example of a handmade paper grid that can be used to equally distribute single leaf discs on a scanner.

    2. Cover the leaf discs placed on the scanner with black paper and obtain a TIFF image of the leaf discs with the scanner set to 1,200 dpi. Include a calibration square (1 x 1 cm) or a ruler in one of the scans that can be used for calibration (x pixels = x mm). When an SLR camera is used, take pictures from a given distance (with a calibration square (1 x 1 cm or a ruler)) in JPEG or JPG format.

  4. Cutting scan image in ImageJ Fiji
    1. Open the scan image acquired in Step C2 in ImageJ Fiji. Click ‘File’ and select ‘Open...’ or drag your selected images from one folder directly to ImageJ Fiji.
    2. Open a macro, click on ‘plugins’; ‘new’; ‘macro’ (Figure 4).


      Figure 4. Opening a new macro in ImageJ Fiji

    3. Copy the following script (only black text) in the macro.ijm.ijm window (Figure 5). Text highlighted in green are comments from the authors and should not be included in the macro. Text in red should be completed by the user.

      n = getNumber(X,X);
      //fill in the number of cuts that has to be made on the spots of the red ‘X’. In case of our acquired scan image: n = getNumber(4,4).
      id = getImageID();
      title = getTitle();
      getLocationAndSize(locX, locY, sizeW, sizeH);
      width = getWidth();
      height = getHeight();
      tileWidth = width / n;
      tileHeight = height / n;
      for (y = 0; y < n; y++) {
      offsetY = y * height / n;
      for (x = 0; x < n; x++) {
      offsetX = x * width / n;
      selectImage(id);
      call("ij.gui.ImageWindow.setNextLocation", locX + offsetX, locY + offsetY);
      tileTitle = title + " [" + x + "," + y + "]";
      run("Duplicate...", "title=" + tileTitle);
      makeRectangle(offsetX, offsetY, tileWidth, tileHeight);
      run("Crop");
      }
      }
      //The images have been cut, but each obtained slice should have a unique name
      imageCount = nImages
      for (image = 1; image <= imageCount; image++) {
      selectImage(image);
      // Changes the title of the active image to a string name
      rename("test" + image);
      }
      //Saves all the slices in the directory you want (marked in red below), make sure to use two backslashes between the folder names.
      n=nImages;
      for(i=0,1; i<n; i++){
      title=getTitle;
      saveAs("TIFF", "X:\\...\\.....\\" + title);
      close();
      }


      Figure 5. Running the macro. The macro cuts the scan image in ImageJ Fiji to obtain images (slices) with single leaf discs.

    4. Run the macro by clicking ‘Run’. ImageJ Fiji now automatically cuts out and saves the single leaf discs as TIFF files in the directory that you have chosen. The single leaf discs are labeled with the title of the scan and a number (Titelscanimage_1, Titelscanimage_2, etc.). ImageJ Fiji starts to cut out leaf discs from the top left corner of the first row, continues to the right and then starts with the next row.
    5. Images produced from the macro (Figure 6) can be further analyzed in Ilastik


      Figure 6. Single leaf disc after cutting scan image with ImageJ Fiji

  5. Training Ilastik to recognize thrips damage
    1. Open Ilastik and create a new project by selecting ‘Pixel classification’ and give your project a name.
    2. Now import the scans with the single leaf discs by clicking ‘Add New’ and select ‘Add separate image(s)…’ (Figure 7). You can now select the images on your computer that should be imported in Ilastik. Image import takes 3.26MB/sec with an Intel® coreTM i7-4910MQ CPU @ 2.90 GHz, RAM 16 GB (as a reference 50 images of each 3.08 MB take 48 sec to be imported).


      Figure 7. Importing images to Ilastik

    3. You can now select features to use for your analysis. Click on the tab ‘Select Features’ and select features. We recommend selecting color/intensity/texture on ơ = 1.0 px (Figure 8).


      Figure 8. Setting parameters for image analysis in Ilastik

    4. Go to the tab ‘Training’, select ‘Add label’ and add 3 labels. One will be for the background, one for the leaf disc and one for the actual thrips damage. The colors will be assigned automatically by the program. Name your labels as shown in Figure 9. Make sure that you keep the same order as shown in the screenshot (Figure 9); this is of importance for later analysis.


      Figure 9. Adding segmentation labels

    5. Now you can start with the training of the program. Select one of the labels and mark the areas that correspond with the labels. It is important to use red to mark areas that are typical thrips damage, with green undamaged leaf areas and with yellow the dark background (Figure 10A). Make sure that you use several leaf discs for the training of the program. You can switch to other leaf discs by choosing the picture number or name from the drop down list next to ‘Current view:’ (Figure 10B). Training is an import step, since the program depends on sufficient training to accurately recognize the different components in the image. Training can take up to half an hour, with a training image processing speed of approximately 0.9 MB/sec (Intel® coreTM i7-4910MQ CPU @ 2.90 GHz, RAM 16 GB). As a reference, approximate 10 cm (with the pencil tool set to 3 pixels) of thrips damaged area marking is necessary for accurate learning.


      Figure 10. Training phase in Ilastik. A. Marking of thrips damage (red), the leaf disc (green) and the background (yellow); B. Switching between imported images in Ilastik.

    6. To check whether the training has been sufficient, click on ‘Live update’. The areas are now colored corresponding to what the program sees as thrips damage (red/pink), the leaf disc (green) and the background (yellow) (Figure 11). If the program does not sufficiently distinguish between thrips damage, leaf disc and background, click again on ‘Life update’ and uncheck ‘Probability’ (marked with redcircle, Figure 11). Add more markers in the picture as specified above and activate ‘Life update’ again. It is advisable to train the program separately for dark and light green leaves, e.g., different leaf ages, accessions or species, to ensure optimal results. In Figure 12 an example of sufficient and insufficient training of Ilastik is provided.


      Figure 11. Live view of image segmentation in the training stage in Ilastik (A)


      Figure 12. Illustration of training in Ilastik. A. The original leaf disc; B. After insufficient training of the leaf disc. In B, thrips damage is overestimated e.g., leaf veins are incorrectly marked as thrips damage and the area right from the leaf vein is also seen as incorrect thrips damage. C. Sufficient training of the leaf disc after marking more area with the correct labels in Ilastik.

  6. Converting images into simple segmentations
    After the training is complete, all imported images can be converted to JPEG files that are simple segmentations (black, grey and white image) of the original pictures.
    1. Go to the tab ‘Prediction Export’, select by ‘Export sources’ ‘Simple segmentation’.
    2. Now go the ‘Export Settings’ and click on ‘Choose settings’. Make sure the settings are the same as in Figure 13. Datatype should be set to ‘signed 8-bit’ and the output file format ‘JPEG’. Make sure you select the location that you want to save your output files in. After you make sure the settings are correct, click ‘OK’. 


      Figure 13. Image export settings after training has been completed in Ilastik

  7. Exporting images after training
    1. Click on ‘Export All’, the program will start with exporting the images. This can take several minutes depending on the processor and RAM module of the computer (0.9 MB/sec with an Intel® coreTM i7-4910MQ CPU @ 2.90 GHz, RAM 16 GB, for reference 50 images of each 3.08 MB take 171 sec to analyze). Exported images will contain thrips damage (red) in black and the leaf disc (green) in grey (Figure 14).


      Figure 14. Exported simple segmentation of the original image. Black = thrips damage and grey = leaf disc.

    2. Do not forget to save your project. If at a later time point you want to process additional scans of single leaf discs with the same settings, proceed to Procedure H. If you are done with processing, close the program and continue with Procedure I.

  8. Processing additional images in Ilastik with the same settings
    1. If you want to process additional scans of single leaf discs using the same settings, make a copy of your project in the same way that you would make a copy of any office file.
    2. Open the copy of your project and go to the tap ‘Batch Prediction Input Selections’. Click on ‘Add new’ and import the additional images that you want to analyse.
    3. Go to the tab ‘Batch Prediction Output Locations’ and make sure you use the same settings for output as described in Step F2. If you keep importing images in the original project, the project becomes very large and demands heavy processing of the computer, making your computer slow. So make sure that you delete the pictures that you have imported in ‘Batch Predicition Input Selection’ before continuing with a new set of images. 

  9. Processing of simple segmentation images in ImageJ Fiji
    In this step we want to extract the grey area and create images that only contain the thrips damage in black.
    1. Open ImageJ Fiji and open the images that you want to further analyze. Click ‘File’ and select ‘Open...’ or drag your selected images from one folder directly to ImageJ Fiji.
    2. Open a new macro as described in Step D1, copy the following script and run the macro (only black text).

      imageCount = nImages
      n=nImages;
      for(i=0,1; i<n; i++){
         setAutoThreshold("Default");
         run("Threshold...");
         setThreshold(0, 70);
         setOption("BlackBackground", false);
         run("Convert to Mask");
      title=getTitle;

      //Give the correct directory where you want to save the output images (marked red)
      saveAs("TIFF", " X:\\...\\.....\\" + title);
      close();
      }

    3. The output images will contain the thrips damage in black (Figure 15) and are labeled the same as the original image, so make sure to save them in a separate folder.


      Figure 15. Image containing only thrips damage after extraction of leaf disc in ImageJ Fjij

  10. Calibration
    Before calculating the amount of damaged surface area we determine how many pixels equal 10 mm.
    1. Open the image that contains the calibration square or ruler in ImageJ Fiji.
    2. Select the strait line option and draw a line of recognizable size in your image (e.g., 10 mm on the ruler) (Figure 16).


      Figure 16. Calibration in ImageJ Fiji. Selecting the straight line tool in ImageJ Fiji (A) and drawing a line (in yellow) that represents 10 mm in a scan image with a ruler (B).

    3. Go to ‘Analyze’ and select ‘Set scale’. ImageJ Fiji produces a window that shows how many pixels equal the length of the drawn line (Figure 17, label ‘Distance in pixels:’). In this example 206.9034 pixels equal 10 mm.


      Figure 17. Window in ImageJ Fiji that shows how many pixels equal 10 mm from Figure 16B

  11. Determining the amount of damaged surface area in mm2
    1. The damaged area can now be quantified in ImageJ Fiji. Go to ‘Process’, select ‘Batch’ and click on ‘Macro’.
    2. Copy the following script into the opened window. Make sure that you fill in the correct scale (marked in red text) that you obtained in Step J3

      setAutoThreshold("Default");
            run("Threshold...");
            setThreshold(129, 255);
      run("Convert to Mask");
      run("Set Scale...", "distance=#pixels known=distance in mm pixel=1 unit=mm");
      run("Analyze Particles...", "show=Nothing clear include summarize")

    3. Select the folder that contains the black and white images that were obtained in Step I3, select a different map for the output (Figure 18). ImageJ Fiji will produce copies of your analyzed images that are saved in the output folder.


      Figure 18. Batch processing in ImageJ Fjij

    4. ImageJ Fiji will produce a summary of the measurements that you can copy to an excel file. It will contain 5 columns: ‘Slice’ (the name of the image), ‘Count’, ‘Total area’ (total thrips damage in mm2), ‘Average Size’ and ‘% Area’ (Figure 19). Copy the produced data in an excel file.


      Figure 19. Output summery in ImageJ Fiji. The output shows the name of the analysed image in the column ‘Slice’ and the total thrips damage (mm2) in the column ‘Total Area’.

Data analysis

Before reporting the thrips damage of your leaf discs, you should correct for the average error that Ilastik makes. This average error is determined by calculating the average thrips damage on the leaf discs with no thrips (control discs). (Table 1)



Corrected data

Corrected thrips damage per leaf disc:
Image_1_T = 20 – 2 = 18
Image_1_T = 15 – 2 = 13
Image_1_T = 17 – 2 = 15

The corrected thrips damage can be used for further statistical analysis.

Table 1. Numerical example of obtained data

Recipes

  1. 1.5% liquid agar solution
    Ad 7.5 g agar to 500 ml water in a 500 ml laboratory bottle.
    Heat the bottle in a microwave until the agar is completely dissolved
    Note: keep the cap lose on the bottle so air can escape. Otherwise the cap might blow off in the microwave due to air pressure buildup in the bottle.

Acknowledgments

This work was supported by the Stichting voor de Technische Wetenschappen (STW) which is part of the Green defense Against Pest (GAP) program, project 13552. Nicole M. van Dam gratefully acknowledges the support of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig funded by the German Research Foundation (FZT 118). This protocol has been used in Entomologia Experimentalis et Applicata (Visschers et al., 2018). The authors have declared no conflicts of interest.

References

  1. Bongiorno, T., Kazlow, J., Mezencev, R., Griffiths, S., Olivares-Navarrete, R., McDonald, J. F., Schwartz, Z., Boyan, B. D., McDevitt, T. C, and Sulchek, T. (2014). Mechanical stiffness as an improved single-cell indicator of osteoblastic human mesenchymal stem cell differentiation. J Biomech 47: 2197-2204.
  2. Fabrowski, P., Necakov, A. S., Mumbauer, S., Loeser, E., Reversi, A., Streichan, S., Briggs, J. A. G. and De Renzis, S. (2013). Tubular endocytosis drives remodelling of the apical surface during epithelial morphogenesis in Drosophila. Nat Commun 4: 2244.
  3. Leiss, K. A., Choi, Y. H., Abdel-Farid, I. B., Verpoorte, R. and Klinkhamer, P. G. (2009). NMR metabolomics of thrips (Frankliniella occidentalis) resistance in Senecio hybrids. J Chem Ecol 35(2): 219-229.
  4. Maharijaya, A., Vosman, B., Steenhuis-Broers, G., Harpenas, A., Purwito, A., Visser, R. G. F. and Voorrips, R. E. (2011). Screening of pepper accessions for resistance against two thrips species (Frankliniella occidentalis and Thrips parvispinus). Euphytica 177: 401-410.
  5. Maharijaya, A., Vosman, B., Verstappen, F., Steenhuis-Broers, G., Mumm, R., Purwito, A., Visser, R. G. F. and Voorrips R. E. (2012). Resistance factors in pepper inhibit larval development of thrips (Frankliniella occidentalis). Entomol Exp Appl 145: 62-71.
  6. Mirnezhad, M., Romero-Gonzalez, R. R., Leiss, K. A., Choi, Y. H., Verpoorte, R. and Klinkhamer, P. G. (2010). Metabolomic analysis of host plant resistance to thrips in wild and cultivated tomatoes. Phytochem Anal 21(1): 110-117.
  7. Morrison, E. B. and Lindell, C. A. (2012). Birds and bats reduce insect biomass and leaf damage in tropical forest restoration sites. Ecol Appl 22: 1526-1534.
  8. Meyer, G. A. and Hull-Sanders, H. M. (2008). Altered patterns of growth, physiology and reproduction in invasive genotypes of Solidago gigantea (Asteraceae). Biol Invasion 10: 303-317.
  9. Rasband, W. S. (2011). ImageJ, US National Institutes of Health, Bethesda, Maryland, USA. https://imagej.nih.gov/ij/.
  10. Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J. Y., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P. and Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7): 676-682.
  11. Sommer, C., Straehle, C., Köthe, U. and Hamprecht, F. A. (2011). Ilastik: Interactive learning and segmentation toolkit. 2011 IEEE International symposium on biomedical imaging: From nano to macro, Institute of Electrical and Electronics Engineers (IEEE), Yoshida Campus, Kyoto University, Kyoto, Japan, pp: 230-233.
  12. Visschers, I. G. S., Dam, N. M. and Peters, J. L. (2018). An objective high-throughput screening method for thrips damage quantitation using Ilastik and ImageJ. Entomologia Experimentalis et Applicata 166(6): 508-515.

简介

昆虫损伤的定量是鉴定植物抗性的重要指标。 在筛选宿主植物对蓟马的抗性时,将总的受损叶面积用作确定抗性水平的标准。 在这里我们提出了一种客观的新方法,用免费提供的软件程序Ilastik和ImageJ来分析叶盘上的蓟马损伤。 该协议的开发是为了筛选超过40个辣椒品系对西方花蓟马和西蓟马蓟马(洋葱蓟马)的抗性。

【背景】昆虫损伤的定量是鉴定植物抗性的重要指标。在筛选宿主植物对蓟马抗性的程序中,总损伤叶面积被用作确定抗性水平的标准。蓟马损害的特点是银色斑点与完整叶片区域显示出高对比度,但喂食点还包括从深绿色到棕色的深色区域。这些叶片的逐渐变色太细微,无法用诸如Winfolia等程序精确量化( http:// www .regentinstruments.com / assets / winfolia_software.html )或ImageJ(Rasband,2011)。结果,蓟马损害通常由评估样品的个体评分。将样本分为表示损害数量的类别(Mirnezhad等人,2010; Maharijaya等人,2011和2012),或损害估计为最接近的1 (Leiss等人,2009; Mirnezhad等人,2010; Maharijaya等人, 2011年和2012年)。这些主观测量使得研究/筛选计划之间的比较困难。而且,它们耗费时间,因此对养殖公司来说代价很高。在这里,我们提出了一个客观的高通量标准化筛选方法,用免费提供的软件程序ImageJ Fiji(Schindelin et al。,2012)和Ilastik(Sommer et ,2011)。 Ilastik具有广泛的应用范围,从细胞生物学(Fabrowski等人,2013),用于计算上皮细胞表面变平的量,生物力学(Bongiorno等,et al。 ,2014),用于识别人类间充质干细胞的边界。它是一种易于使用的自学图像处理程序,可根据用户提供的标签对二维曲面进行分割和分类(Sommer et al。,2011)。 ImageJ通常用于量化咀嚼食草动物和完整叶片的总叶面积(Meyer和Hull-Sanders,2008; Morrison和Lindell,2012)的去除叶面积的量。然而,它很少用于量化由蓟马引起的摄食损伤。蓟马摄食会在叶子上造成相当微妙的变色。 ImageJ在量化这种色差方面受到限制,为此Ilastik提供了更适合的选择。

关键字:昆虫耐药性, 昆虫危害, 图像分析

材料和试剂

  1. 过滤纸(例如,过滤纸600号,VWR,目录号:516-0309)
  2. (例如,18×25厘米,50微米聚乙烯foliezak遇到druksluiting,Vink Lisse,目录号:174718 49)
  3. 培养皿直径15厘米(例如,未处理的培养皿,康宁,目录号:430597)
  4. Parafilm M( eg ,BRAND ,Parafilm ,VWR,产品目录号:291-1213)< br />
  5. 玻璃瓶(图1)(例如,555ml带Twist-off装置的Twist-off罐TO82具有BPA NI盖, www.glazenflessenenpotten.nl ,GLAZEN RLESSEN EN POTTEN。NL,目录号:V2391WS和VC305)


    图1.用于蓟马饥饿的玻璃瓶(高11.5厘米×直径7.5厘米)

  6. 同步的L1 / L2 Frankliniella occidentalis (Pergande)或<蓟马烟草(Lindeman)(缨翅目)

  7. 辣椒(Solanaceae)植物(任何品种)
  8. 琼脂(例如,Phyto琼脂,Duchefa Biochemie,目录号:P1003)

  9. 1.5%液体琼脂溶液(见食谱)

设备

  1. 对于 F,气候柜设置为25°C。 occidentalis 或23°C for T。 tabaci ,L16:D8灯光区域( ,带有TL闪电的经济型Delux 432 L,Snijders Labs, http://www.snijderslabs.com )
  2. 微波炉(Moulinex,型号:微型厨师FM2515Q)
  3. 直径1.5厘米的软木钻孔器(例如,带手柄的洪堡黄铜软木钻套装,Fisher Scientific,目录号:07-865-10B)
    制造商:Humboldt Mfg。,目录号:H-9663。
  4. 烧杯50ml(例如,Griffin烧杯,Corning,PYREX®,目录号:1000-50)
  5. 软油漆刷(,例如,van Eyck油漆刷套装,1号刷子)
  6. 塑料镊子(例如,Azlon镊子 - 镊子,Dynalab Corp.,Dynalon Labware,产品目录号:516555-0001, https://www.dynalon.com/PublicStore/ )
  7. 爱普生10000XL扫描仪(爱普生,型号:10000XL)或任何单反相机(1200万像素)与三脚架
  8. 2厘米间距的手工格子(图3)
  9. 黑纸(用于扫描仪)或黑布(用于单反相机)
  10. 已安装软件的笔记本电脑
  11. 精密天平(Sigma-Aldrich,产品目录号:Z267074)
    制造商:Sartorius,型号:BP 310 S。
  12. 实验室瓶盖500毫升(DWK生命科学,杜兰,目录号:21 801 44 5)

软件

  1. ImageJ Fiji 例如,版本2.0.0与Java 1.6.0_24
  2. Ilastik版本1.1.3,
    注:为了成功应用该协议,使用此确切版本非常重要。该软件可以在网上找到: files.Ilastik.org/1.1/ ,'ilastik-1.1.3 -win64.exe“,也可用于Linux和OSX。
  3. Epson Scan Utility 例如,版本3.4.9.9
    注意:只有在使用Epson扫描仪或使用其他扫描仪时才需要。

程序

  1. 蓟马制剂
    收集同步的L1 / L2幼虫在实验前24小时内衬有3片稍湿润滤纸的玻璃瓶中。将玻璃瓶放入温度设定为25°C的气候柜中。 occidentalis 或23°C for T。 tabaci ,带有L16:D8灯光系统。这个24小时的饥饿期确保了在无选择叶盘测定开始后蓟马将开始直接进食。

  2. 无选择的叶盘测定法
    1. 准备1.5%液体琼脂溶液(见配方1)

    2. 收集温室植物的叶子并运送到实验室,使用密封的类似Ziploc的塑料袋盛装2毫升水,以保持叶片新鲜。

    3. 。用微波炉加热琼脂,直至液化
    4. 使用软木塞凿子冲压叶盘,同时避开中脉。
      中等蛋白通常有浅色块,可能导致不准确的蓟马损伤量化。
    5. 将一部分琼脂溶液倒入烧杯中,并在培养皿中间倒入一小滴(约2厘米直径)的液体琼脂(图2A)。将叶盘置于琼脂上,使正面与琼脂完全接触。这确保蓟马只能在叶的远轴侧(“下侧”)进食。
      在琼脂变成固体之前将叶盘置于琼脂上(凝胶化温度在34-36℃之间,Duchefa Biochemie Safety Data Sheet P1003 2.0版)。
    6. 用一把小画笔在每个叶片上放置五个蓟马(图2B)。
      在将蓟马置于叶片上之前,确保琼脂完全固化
    7. 用Parafilm <®M>关闭培养皿并将它们置于气候箱中48小时(图2C)。
    8. 48小时后,可以用小画笔去除蓟马,并可以获取叶盘的数字图像。&nbsp;


      图2.叶盘试验的实验装置将叶盘置于琼脂(A)上,接种蓟马(B)并将闭合的培养皿置于气候箱(C)中的托盘中, 。

  3. 获取图像
    1. 将带有塑料镊子的叶片放在扫描仪上(如果使用单反相机,请将其放在黑布上)。
      1. 为了便于处理获取的图像,所有的叶片应该均匀分布,以便在ImageJ斐济使用一次单一的宏观运行就可以很容易地“切割”叶片。叶盘的均匀分布可以使用网格来实现(图3)。
      2. 放置在水平和垂直轴上的物体(叶片和标签)的总数应该相等(在我们的例子中:水平轴:3片叶片+1标签,垂直轴:4片叶片)。
      3. 当您使用协议时,建议包括未暴露于蓟马损伤的叶片(对照叶片),并分析这些叶片的方式与暴露于蓟马的叶片相同。 Ilastik有时高估了损伤的数量,例如,由于叶脉的存在。
        未损坏的叶盘图像可用于纠正这些错误。&nbsp;


        图3.放置叶片。 A.使用扫描仪设置为1,200 dpi的TIFF图像示例。在这个例子中,水平轴和垂直轴用白色虚线标出以便说明。标尺包含在图像右侧进行校准。 B.可用于在扫描仪上平均分配单片光盘的手工纸张网格示例。

    2. 使用黑纸覆盖放置在扫描仪上的叶片,并将扫描仪设置为1,200 dpi时获得叶片的TIFF图像。在可用于校准的扫描之一中包含校准方块(1 x 1厘米)或标尺(x像素= x毫米)。当使用单反相机时,以JPEG或JPG格式从给定距离拍摄照片(使用校正方块(1 x 1厘米或尺子))。

  4. 在ImageJ斐济切割扫描图像
    1. 打开ImageJ斐济步骤C2中获取的扫描图像。点击'文件',然后选择'打开...'或将选定的图像从一个文件夹直接拖到ImageJ斐济。
    2. 打开一个宏,点击'插件'; '新'; '宏'(图4)。


      图4.在ImageJ斐济开设新宏

    3. 在macro.ijm.ijm窗口中复制以下脚本(仅黑色文本)(图5)。以绿色突出显示的文本是来自作者的评论,不应包含在宏中。红色文本应该由用户完成。

      n = getNumber( X,X );
      //填写必须在红色'X'的斑点上进行的切割次数。如果我们获得了扫描图像:n = getNumber(4,4)。
      id = getImageID();
      title = getTitle();
      getLocationAndSize(locX,locY,sizeW,sizeH);
      width = getWidth();
      height = getHeight();
      tileWidth = width / n;
      tileHeight = height / n;
      for(y = 0; y
      offsetY = y * height / n;
      for(x = 0; x
      offsetX = x * width / n;
      selectImage(id);
      call(“ij.gui.ImageWindow.setNextLocation”,locX + offsetX,locY + offsetY);
      tileTitle = title +“[”+ x +“,”+ y +“]”;
      run(“Duplicate ...”,“title =”+ tileTitle);
      makeRectangle(offsetX,offsetY,tileWidth,tileHeight);
      run(“Crop”);
      }
      }
      / / font-family:Courier New;“>
      imageCount = nImages
      for(image = 1; image <= imageCount; image ++){
      selectImage(image);
      //将活动图像的标题更改为字符串名称
      重命名(“test”+图片);
      }
      //将所有切片保存在您想要的目录中(以红色标记),确保在文件夹名称之间使用两个反斜杠。 span>
      n = nImages;
      for(i = 0,1; i&lt; n; i ++){
      title = getTitle;
      saveAs(“TIFF”, X:\\ ... \\ ..... \ + title); > close();
      }


      图5.运行宏。宏将ImageJ斐济的扫描图像剪切成单张叶片图像(切片)。

    4. 点击'运行'运行宏。 ImageJ斐济现在会自动剪切并将单片光盘作为TIFF文件保存在您选择的目录中。单片光盘标有扫描标题和一个数字(Titelscanimage_1,Titelscanimage_2, etc。)。 ImageJ斐济开始从第一排左上角切出叶片,继续向右,然后从下一排开始。
    5. 从宏观产生的图像(图6)可以在Ilastik
      中进一步分析

      图6.使用ImageJ Fiji剪切扫描图像后的单片叶片

  5. 培训Ilastik识别蓟马损害
    1. 打开Ilastik并通过选择“像素分类”并为您的项目命名来创建一个新项目。
    2. 现在通过单击“添加新的”导入带有单片光盘的扫描并选择“添加单独的图像...”(图7)。现在,您可以选择要在Ilastik中导入的计算机上的图像。图像输入速度为3.26MB /秒,采用Intel核心处理器i7-4910MQ CPU @ 2.90 GHz,RAM 16 GB(作为参考50张图像,每个3.08 MB需48张秒导入)。


      图7.将图像导入Ilastik

    3. 您现在可以选择用于分析的功能。点击“选择功能”选项卡并选择功能。我们建议在ơ= 1.0 px(图8)上选择颜色/强度/纹理。


      图8.为Ilastik中的图像分析设置参数

    4. 转到标签'训练',选择'添加标签'并添加3个标签。一个将用于背景,一个用于叶盘,另一个用于实际的蓟马损害。颜色将由程序自动分配。如图9所示为您的标签命名。确保您保持相同的顺序,如屏幕截图所示(图9);这对于以后的分析很重要。


      图9.添加分段标签

    5. 现在您可以从培训计划开始。选择其中一个标签并标记与标签相对应的区域。使用红色来标记典型的蓟马损害区域,绿色未损坏的叶片区域和黄色的深色背景(图10A)。确保您使用多片叶片来培训课程。您可以通过从“当前视图:”旁边的下拉列表中选择图片编号或名称来切换到其他叶片光盘(图10B)。培训是一个重要的步骤,因为程序依赖于足够的培训来准确识别图像中的不同组件。训练可能需要半小时,训练图像处理速度大约为0.9 MB /秒(Intel核心处理器TM i7-4910MQ CPU @ 2.90 GHz,RAM 16 GB)。作为参考,蓟马受损区域标记大约10厘米(铅笔工具设置为3像素)对于准确学习是必需的。


      图10.在Ilastik中的训练阶段A.标记蓟马损伤(红色),叶盘(绿色)和背景(黄色); B.在Ilastik中导入的图像之间切换。

    6. 要检查训练是否足够,请点击' Live 更新'。现在这些区域的颜色对应于程序看到的蓟马损伤(红色/粉红色),叶片(绿色)和背景(黄色)(图11)。如果程序没有充分区分蓟马损伤,叶片和背景,请再次单击“生命更新”并取消选中“概率”(用红圈标记,图11)。如上所述在图片中添加更多标记并再次激活“生命更新”。对于黑暗和浅绿色的叶子,例如不同的叶片年龄,种质或品种,分别培养程序以确保最佳结果是明智的。在图12中提供了对Ilastik足够和不足的培训的例子。


      图11. Ilastik(A)培训阶段图像分割的实时视图


      图12. Ilastik培训示例A.原始叶片; B.在叶盘训练不足之后。在B中,蓟马损害被高估,例如,叶脉不正确地被标记为蓟马损害,并且叶脉上的区域也被视为不正确的蓟马损害。 C.在用Ilastik中的正确标签标记更多区域后,对叶片进行充分培训。

  6. 将图像转换为简单的分段
    训练完成后,所有导入的图像都可以转换为JPEG文件,这些文件是原始图片的简单分段(黑色,灰色和白色图像)。
    1. 转到“预测导出”选项卡,选择“导出源”“简单分段”。
    2. 现在转到“导出设置”并点击“选择设置”。确保设置与图13中的相同。数据类型应该设置为“signed 8-bit”,输出文件格式为“JPEG”。确保您选择要保存输出文件的位置。确保设置正确后,点击“确定”。&nbsp;


      图13.在Ilastik
      完成训练后的图像输出设置
  7. 训练后导出图像
    1. 点击'全部导出',程序将开始导出图像。这可能需要几分钟时间,具体取决于计算机的处理器和RAM模块(采用Intel Core™CPU i7-4910MQ @ 2.90 GHz时为0.9 MB /秒,RAM 16 GB,每个3.08 MB的50张图像需要171秒来分析)。导出的图像将包含黑色的蓟马损伤(红色)和灰色的叶片(绿色)(图14)。


      图14.导出原始图像的简单分割。黑色=蓟马损伤,灰色=叶片。

    2. 不要忘记保存你的项目。如果在稍后的时间点想要使用相同的设置处理单张光盘的其他扫描,请转至步骤 H 。如果您已完成处理,请关闭程序并继续操作步骤我。

  8. 使用相同的设置处理Ilastik中的其他图像
    1. 如果您想要使用相同的设置处理单张光盘的其他扫描,请使用与制作任何办公文件副本相同的方式制作项目的副本。
    2. 打开项目的副本,然后点击“批量预测输入选择”。点击“添加新的”并导入您想要分析的附加图像。
    3. 转到“批量预测输出位置”选项卡,并确保您按照步骤 F2 中所述使用相同的输出设置。如果您继续在原始项目中导入图像,该项目会变得非常大,并且需要对计算机进行大量处理,从而使您的计算机变慢。因此,在继续使用一组新图像之前,请确保您删除了在“批量预测输入选择”中导入的图片。&nbsp;

  9. 在ImageJ斐济处理简单的分割图像
    在这一步中,我们要提取灰色区域并创建仅包含黑色蓟马损伤的图像。
    1. 打开ImageJ斐济并打开想要进一步分析的图像。点击'文件',然后选择'打开...'或将选定的图像从一个文件夹直接拖到ImageJ斐济。
    2. 按步骤 D1 中所述打开新宏,复制以下脚本并运行宏(仅限黑色文本)。

      imageCount = nImages
      n = nImages;
      for(i = 0,1; i&lt; n; i ++){
      &nbsp;&nbsp; setAutoThreshold(“Default”);
      &nbsp;&nbsp;运行(“阈值...”);
      &nbsp;&nbsp; setThreshold(0,70);
      &nbsp;&nbsp; setOption(“BlackBackground”,false);
      &nbsp;&nbsp;运行(“转换为蒙版”);
      title = getTitle;

      //给出想要保存输出图像的正确目录(标记为红色)
      saveAs(“TIFF”,“ X:\ \ ... \\ ... \\ “+ title);
      close();
      }

    3. 输出图像将包含黑色蓟马损伤(图15),并标记为与原始图像相同,因此请确保将它们保存在单独的文件夹中。


      图15.在ImageJ Fjij中提取叶盘后仅包含蓟马损害的图像

  10. 校准
    在计算损坏表面积的数量之前,我们确定有多少像素等于10毫米。

    1. 在ImageJ斐济打开包含校准正方形或标尺的图像。
    2. 选择strait line选项,并在图像中绘制一系列可识别的尺寸(例如,标尺上为10 mm)(图16)。


      图16. ImageJ Fiji中的校准在ImageJ Fiji(A)中选择直线工具,并用尺子(B)在扫描图像中绘制代表10 mm的直线(用黄色)。< br />
    3. 转到“分析”并选择“设置比例”。 ImageJ斐济生成一个窗口,显示有多少像素等于所画线条的长度(图17,标签'Distance in pixels:')。在这个例子中,206.9034像素等于10毫米。


      图17. ImageJ斐济的窗口显示了与图15B相距10毫米的像素数

  11. 以毫米2确定受损表面积的数量2 / sup>
    1. ImageJ斐济现在可以对受损区域进行量化。转到'处理',选择'批',然后点击'宏'。
    2. 将以下脚本复制到打开的窗口中。确保您填写了在 J3 中获得的正确比例(用红色文本标记)。&nbsp;

      setAutoThreshold(“Default”);
      &nbsp; &NBSP; &NBSP;运行(“阈值...”);
      &nbsp; &NBSP; &NBSP; setThreshold(129,255);
      run(“Convert to Mask”);
      run(“Set Scale ...”,“distance = #像素 已知= distance in mm pixel = 1 unit = mm”);
      run(“Analyze Particles ...”,“show = Nothing clear include summary”)

    3. 选择包含步骤 I3 中获取的黑白图像的文件夹,为输出选择不同的映射(图18)。 ImageJ斐济将生成保存在输出文件夹中的分析图像的副本。


      图18. ImageJ Fjij中的批处理

    4. ImageJ斐济将提供您可以复制到Excel文件的测量摘要。它将包含5列:'切片'(图像名称),'计数','总面积'(总蓟马损伤以毫米2为单位),'平均尺寸'和'%面积' (图19)。将生成的数据复制到excel文件中。


      图19. ImageJ Fiji中的输出总结。输出显示列'Slice'中分析图像的名称以及列中总蓟马损伤(mm 2 ) '总面积'。

数据分析

在报告叶片蓟马损坏之前,您应该纠正Ilastik制造的平均误差。这个平均误差是通过计算没有蓟马的叶盘上的平均蓟马损伤(对照圆盘)来确定的。 (表1)


更正资料

修正每片叶片的蓟马损害:
Image_1_T = 20 - 2 = 18
Image_1_T = 15 - 2 = 13
Image_1_T = 17 - 2 = 15

修正后的蓟马损害可用于进一步的统计分析。

表1.获得的数据的数字示例

食谱

  1. 1.5%液体琼脂溶液

    在500毫升的实验室瓶中,将7.5克琼脂加入500毫升水中。
    用微波加热瓶子直至琼脂完全溶解
    注意:请将瓶盖丢在瓶子上,以免空气流失。否则由于气瓶内压力的增加,盖子可能会在微波炉中吹出。

致谢

这项工作得到了Stichting voor de Technische Wetenschappen(STW)的支持,该项目属于绿色防治害虫(GAP)项目1355项目的一部分。Nicole M. van Dam衷心感谢德国综合生物多样性研究中心(iDiv )由德国研究基金会(FZT 118)资助的Halle-Jena-Leipzig。该方案已被用于Entomologia Experimentalis et Applicata(Visscher等人,<2018>)。该作者声明没有利益冲突。

参考

  1. Bongiorno,T.,Kazlow,J.,Mezencev,R.,Griffiths,S.,Olivares-Navarrete,R.,McDonald,JF,Schwartz,Z.,Boyan,BD,McDevitt,T.C和Sulchek,T 。(2014)。 机械僵硬度作为改善成骨细胞人类间充质干细胞分化的单细胞指标。 J Biomech 47:2197-2204。
  2. Fabrowski,P.,Necakov,A. S.,Mumbauer,S.,Loeser,E.,Reversi,A.,Streichan,S.,Briggs,J.A.G。和De Renzis,S。(2013)。 管状内吞作用驱动果蝇中上皮形态发生过程中顶端表面的重塑。 Nat Commun 4:2244.
  3. Leiss,K.A.,Choi,Y.H。,Abdel-Farid,I.B.,Verpoorte,R.和Klinkhamer,P.G。(2009)。 蓟马( Frankliniella occidentalis )的核磁共振代谢组学在Senecio hybrids。 J Chem Ecol 35(2):219-229。
  4. Maharijaya,A.,Vosman,B.,Steenhuis-Broers,G.,Harpenas,A.,Purwito,A.,Visser,R.G.F和Voorrips,R.E。(2011)。 筛选辣椒种质对两种蓟马物种的抗性( Frankliniella occidentalis 和蓟马parvispinus )。 Euphytica 177:401-410。
  5. Maharijaya,A.,Vosman,B.,Verstappen,F.,Steenhuis-Broers,G.,Mumm,R.,Purwito,A.,Visser,R. G. F.和Voorrips R.E。(2012)。 胡椒中的抗性因子抑制蓟马的幼虫发育( Frankliniella occidentalis )。 Entomol Exp Appl 145:62-71。
  6. Mirnezhad,M.,Romero-Gonzalez,R.R.,Leiss,K.A.,Choi,Y.H。,Verpoorte,R。和Klinkhamer,P.G。(2010)。 野生和栽培西红柿中寄主植物对蓟马的抗性代谢组学分析 Phytochem Anal 21(1):110-117。
  7. Morrison,E.B。和Lindell,C.A。(2012)。 鸟类和蝙蝠减少热带森林恢复地点的昆虫生物量和叶片损害。 Ecol Appl 22:1526-1534。
  8. Meyer,G.A。和Hull-Sanders,H.M。(2008)。 改变(一枝黄花)入侵基因型的生长,生理和繁殖模式。生物入侵 10:303-317。
  9. Rasband,W. S.(2011)。 ImageJ,美国国立卫生研究院,美国马里兰州贝塞斯达 https:// imagej.nih.gov/ij/。
  10. Schindelin,J.,Arganda-Carreras,I.,Frize,E.,Kaynig,V.,Longair,M.,Pietzsch,T.,Preibisch,S.,Rueden,C.,Saalfeld,S.,Schmid,B Tinevez,JY,White,DJ,Hartenstein,V.,Eliceiri,K.,Tomancak,P.和Cardona,A。(2012)。 斐济:生物图像分析的开源平台。 Nat方法 9(7):676-682。
  11. Sommer,C.,Straehle,C.,Köthe,U.和Hamprecht,F.A。(2011)。 Ilastik:交互式学习和分割工具包 2011年IEEE生物医学成像国际研讨会: 从纳米到宏观,电气和电子工程师协会(IEEE),吉田校区,京都大学,日本京都,页数:230-233。
  12. Visschers,I.G.S。,van Dam,N.M。,Peters,J.L。(2018)。 使用Ilastik和ImageJ对蓟马损伤定量的客观高通量筛选方法。 Entomol Exp Appl (正在按)。
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引用:Visschers, I. G. S., van Dam, N. M. and Peters, J. L. (2018). Quantification of Thrips Damage Using Ilastik and ImageJ Fiji. Bio-protocol 8(8): e2806. DOI: 10.21769/BioProtoc.2806.
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