Simple Digital Photography for Assessing Biomass and Leaf Area Index in Cereals

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Journal of Integrative Plant Biology
Jan 2014



These instructions refer to obtaining fast and low-labour estimates of ground cover, leaf area index and green biomass for a large number of plots, as those encountered in cereal breeding programs. The procedure includes obtaining the pictures in the field and processing them once downloaded to a computer.

Keywords: Image processing (图像处理), Biomass assessment (生物量评估), Non-destructive methods (非破坏性的方法), Leaf Area Index (叶面积指数)

Materials and Reagents

  1. Crop
    Note: The crop must be free of green weeds. The presence of spikes, flowers, etc. on the external part of the canopy may require using a specific calibration curve for that development stage.


  1. Conventional digital camera.
    Note: This is the only equipment required once the method has been calibrated for a range of crops similar to those where it has to be applied. Any commercial camera, compact or reflex, is suitable.
  2. A digital area meter is required for calibrating leaf area index (LAI, e.g. DIAS II, Delta-T Devices)
  3. A drying oven is required for calibrating biomass (e.g. Memmert, model: 800 , minimum precision required: 2 °C)
  4. A precision balance is required for calibrating biomass (e.g. Mettler Toledo, model: PB3002-L , minimum precision required: 0.1 g)


  1. Image processing software that allows quantifying the average colour of the whole picture
    Note: The program BreedPix 2.0 described in Casadesús and Villegas (2014) is a suitable tool for processing a large number of pictures with the minimal required steps by the user. It can be obtained for free from the authors. General-purpose free software for quantitative image analysis (such as ImageJ or Fiji) can also be used but in this case a macro should be written specifically for this protocol.


The pictures must be obtained under stable lighting conditions for the whole sampling session. This can be either under clear sky or under moderately cloudy conditions if these are equivalent for all the plots. Also, to avoid differences in the length of shadows in the pictures the time of day would be preferably ± 3 h around solar noon. Wind conditions should be avoided because bent canopies may expose to the camera a different amount of their leaf area and different parts of the plants than in normal conditions.

  1. Acquiring the images in the field
    1. Predefine the structure and order of the series of photographs.
      In order to facilitate the identification of each image, the plots of the field trial must be photographed following a predefined plan (Figure 1). The plan must also specify the number of photographs to take at each plot, a decision that would depend on the area/ size of the plots and their internal heterogeneity. For instance, for plots under 2 m2 only one image is feasible to obtain, while for a plot area in the range of 20 m2, the number of photographs may be between 3 and 5 depending on the internal heterogeneity of the plots. If a plot is highly regular, a lower number of pictures may be representative. On the other hand, plots with different plant densities will need more pictures in order to account for all the plants contained in it. It is advised to include regularly in the series of photographs some additional pictures to help confirm the identification of the neighbouring images. For instance, pictures of some label, the perspective of the field trial from the current position, etc. will be useful for verifying the position of each photograph within the field when they will be downloaded for analysis.

      Figure 1. A) Orthoimage of a typical experimental or breeding field experiment with several plots B) Field experimental map C) Possibility 1 of pre-defined path D) Possibility 2 of pre-defined path. Within the maps, “B” indicates “border plot”.

    2. Camera settings.
      The camera settings can either be left to the default options or optimized according to the photographer’s criteria but must be the same for all pictures. Under stable lighting conditions the settings can be set to automated mode. The focal length is not expected to have a remarkable effect on these assessments.
      The zoom should be fixed -the same for all pictures- to one position which allows a wide view of the plot without including any part external to the sampled plot - e.g. neighbour plots, spacing between plots, photographer’s feet, etc.
      The images can be saved in JPG format, at an image size equal or larger than 1024 x 768 pixels. The only problem with large images is that they require more memory in the camera and will take longer to process in the computer.
    3. Taking the pictures.
      The photographer will follow the plots according to the sequence specified in the sampling plan. The camera must be held oriented zenithally - that is, aiming exactly downwards. The pictures can be obtained holding the camera with one hand, with the photographer’s arm fully extended ahead, above the canopy, at shoulder height. Care must be taken to not include the photographer’s feet in the image. Also, the gap between two plots should not appear in the picture.

  2. Processing the images
    Once the images are downloaded to the computer, image-processing software must be used for calculating one or more vegetation indices for each of the images. The easiest index to quantify and to interpret is the Green Fraction (GF), which in early stages of the crop is closely related with ground cover and it can be calculated as the proportion of green pixels to the total number of pixels of the image.
    GF = green pixels/total pixels
    The recommended scope for using this method in winter cereals is for early stages above anthesis. At later stages the spikes have an effect on the properties of the canopies and, also, vegetation indices use to become saturated when LAI is above 3 (Aparicio et al., 2000).

    Figure 2. Process of obtaining the photographic sampling at the field, processing the images to calculate some vegetation indices and converting those indices to indirect assessments of LAI or green biomass

    The classification of the pixels as green can be based on its Hue. When pixel colours are expressed in the HIS colour coordinates (for Hue, Intensity, and Saturation), green pixels are those whose Hue value ranges between 60º and 180º (approximately between yellow-lime and turquoise). Other vegetation indices based on photographs are exposed in Casadesus et al. (2007) and Casadesus and Villegas (2014). Briefly, once each of the pixels in an image has been classified according to its Hue as either green vegetation or background, GF can be quantified as the ratio of green vegetation pixels to total pixels.
    The program BreedPix 2.0 (Casadesus and Villegas, 2014) can calculate the GF of hundreds of images within seconds after downloading the camera to a computer. In this software, the user just has to select the folder that contains the image files to process. Then it will calculate the GF corresponding to each photograph and it will write the results in an output file for the whole list of photographs. Optionally, the user can indicate the path followed to obtain the pictures, which will facilitate identifying the genotype at each plot. As an alternative to BreedPix, most tools for quantitative image analyses (e.g. ImageJ, Fiji, etc.) include methods for counting how many pixels lay on a given range of colours and this feature can be used to quantify green pixels on individual images or portions of the image.

  3. Indirect assessment of LAI and Green Biomass
    The image-processing software will calculate a vegetation index for each picture which is strongly correlated with both LAI and Green Biomass. In order to obtain an estimate of the LAI or the Green Biomass of that plot it is necessary to have a calibration curve for a similar plant material where to interpolate the value for the vegetation index. For instance, all winter cereals (wheat, barley, oats, etc.) could share a common calibration curve before anthesis. Instead, an alfalfa crop should have a different calibration curve due to a different leaf distribution within the canopy. To construct a calibration curve for a particular plant material it is necessary to harvest the samples of that crop (immediately after the photograph) with different plant densities or at different developing stages, for the destructive lab-determination of LAI and/or green biomass. Determination of LAI requires measuring with a leaf-area meter the area of the green leaves (one side laminae). Determination of green biomass in terms of crop dry weight (CDW) requires weighing jointly the sampled green parts - leaves, stems and spikes if present– after oven-drying them at 70 °C for 48 h. In all cases, a representative sample of the plot must be measured, of at least all the plants contained in a 0.075 m2 section of the plot. A representative sample may be chosen at random after discarding two zones: the 10% of the plot with higher biomass and the 10% of the plot with lower biomass, both estimated visually.
    For instance, in winter cereal crops the calibration curve should contain points from seedling (Zadoks stage 12) to anthesis (Zadoks stage 65). The plant densities must cover the expected range where the calibration will be used.
    An example of calibration curve for winter cereals at pre-anthesis is shown in Figure 5, based on Casadesus and Villegas (2014). From that example, the calculation for converting GF to LAI is:
    LAI = 3.164 x GF - 0.143
    Based on the same source, the calculation of CDW is:
    CDW = 440.2 x GF - 56.81

Representative data

  1. Figure 3 is an example of how the pictures should look like. Figure 4 shows a sample of several pictures obtained in a sampling session. Figure 5 shows an example of calibration curve for LAI.

    Figure 3. Example of one zenithal photograph valid for assessing green biomass

    Figure 4. Example of the sequence of pictures obtained in a sampling session. In this case, there were 6 images per plot, with an additional image between plots- showing a perspective of the field trial- to help identify the neighbouring images. This figure shows only part of the sample; the whole session may include some hundreds of images.

    Figure 5. Example of a general calibration curve for LAI in winter cereals. GF was calculated from photographs obtained just before sampling destructively for LAI in 24 plots including bread wheat, triticale and tritordeum, at two different dates before anthesis.


This protocol was adapted from Casadesus et al. (2007) and Casadesús and Villegas (2014).


  1. Aparicio, N., Villegas, D., Casadesús, J., Araus, J. L. and Royo, C. (2000). Spectral vegetation indices as non-destructive tools for determining durum wheat yield. Agron J 92: 83-91.
  2. Casadesús, J. and Villegas, D. (2014). Conventional digital cameras as a tool for assessing leaf area index and biomass for cereal breeding. J Integr Plant Biol 56(1): 7-14.
  3. Casadesus, J., Kaya, Y., Bort, J., Nachit, M. M., Araus, J. L., Amor, S., Ferrazzano, G., Maalouf, F., Maccaferri, M., Martos, V., Ouabbou, H. and Villegas, D. (2007). Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments. Ann Appl Biol 150, 227-236.
  4. Zadoks, J. C., Chang, T. T. and Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Res 14, 415-421.


这些说明涉及获得对谷物育种计划中遇到的大量地块的地面覆盖,叶面积指数和绿色生物量的快速和低劳动估计。 该过程包括获得现场中的图片并且一旦下载到计算机就对其进行处理。

关键字:图像处理, 生物量评估, 非破坏性的方法, 叶面积指数


  1. 裁剪
    注意:作物必须不含绿色杂草。 在冠层的外部部分上存在尖峰,花等可能需要在该发展阶段使用特定的校准曲线。


  1. 传统数码相机。
    注意:一旦该方法已经针对类似于其必须应用的一系列作物进行校准,这是所需的唯一设备。 任何商业相机,紧凑或反射都是适合的。
  2. 需要数字面积计来校准叶面积指数(LAI,例如,DIAS II,Delta-T装置)
  3. 需要干燥炉来校准生物质(例如Memmert,型号:800,要求最小精度:2℃)。
  4. 需要精确平衡来校准生物质(例如,Mettler Toledo,型号:PB3002-L,所需的最小精度:0.1g)。


  1. 允许量化整幅图像的平均颜色的图像处理软件
    注意:在Casadesús和Villegas(2014)中描述的程序BreedPix 2.0是一个合适的工具,用于处理大量的图片与用户最少的步骤。 它可以从作者免费获得。 也可以使用用于定量图像分析的通用免费软件(例如ImageJ或Fiji),但在这种情况下,应为此协议专门编写宏。



  1. 获取字段中的图像
    1. 预定照片系列的结构和顺序 在 为了便于识别每个图像,绘图的 现场试验必须按照预定计划拍摄(图1)。  计划还必须指定每次拍摄的照片数量 情节,决定将取决于地块和面积的大小 他们的内部异质性。例如,对于仅2m 2 的地块 一个图像是可行的,而对于一个绘图区域在范围内 20m 2 ,照片的数量可以在3和5之间,取决于  内部异质性的图。如果情节非常规则,a 较低数目的图片可以是代表性的。另一方面,情节  与不同的植物密度将需要更多的图片,以便 说明其中包含的所有植物。建议包括 定期在系列照片一些额外的图片帮助 确认相邻图像的识别。例如, 图片的一些标签,从现场试验的角度 当前位置,等将有助于验证每个的位置  照片,当他们将被下载用于分析

      图1。 A)典型实验或繁殖场的正交图像 实验图B)现场实验图C)可能性1  的预定义路径D)可能性2的预定义路径。内 地图,"B"表示"边框图"
    2. 相机设置。
      的 相机设置可以保留为默认选项或优化 根据摄影师的标准,但必须是一样的所有 图片。 在稳定的照明条件下,可以将设置设置为 自动模式。 焦距不会有显着的 对这些评估的影响 缩放应该是固定的 - 相同 所有图片 - 到一个位置,允许广泛的视图的情节 而不包括采样图外部的任何部分 - 例如邻居   绘图,地块之间的间距,摄影者的脚,等。
      图像 可以以JPG格式保存,图像大小等于或大于1024 x   768像素。 大图像的唯一问题是它们需要 更多的内存在相机和将需要更长的时间来处理 计算机。
    3. 拍摄照片。
      摄影师会跟着 根据抽样计划中指定的顺序绘制图表。的 相机必须保持朝向天顶 - 即,准确瞄准 向下。图片可以获得拿着相机与一个 手,与摄影师的手臂完全向前延伸,在上面 冠层,在肩高。必须小心不要包括 摄影师的脚在图像。此外,两个地块之间的差距应该  不出现在图片中。

  2. 处理图像
    GF =绿色像素/总像素
    在冬季谷物中使用这种方法的推荐范围是在开花前的早期阶段。在后期阶段,尖峰对檐篷的性质有影响,而且当LAI 高于3(Aparicio等人,2000)。


    程序BreedPix 2.0(Casadesus和Villegas,2014)可以在将相机下载到计算机后的几秒钟内计算成百个图像的GF。在这个软件中,用户只需要选择包含要处理的图像文件的文件夹。然后它将计算对应于每张照片的GF,并将结果写入整个照片列表的输出文件中。可选地,用户可以指示获得图片所遵循的路径,这将有助于在每个图上识别基因型。作为BreedPix的替代,大多数用于定量图像分析(例如 ImageJ,Fiji,等)的工具包括用于计算给定范围的颜色上的像素数的方法,此功能可用于量化单个图像或图像部分上的绿色像素。

  3. 间接评估LAI和绿色生物量
    图像处理软件将计算与LAI和绿色生物质都密切相关的每个图像的植被指数。为了获得该图的LAI或绿色生物质的估计,有必要具有用于内插植被指数的值的类似植物材料的校准曲线。例如,所有冬季谷物(小麦,大麦,燕麦,等)可以在开花前共享共同的校准曲线。相反,苜蓿作物应该具有不同的校准曲线,由于在冠层内的不同的叶分布。为了构建特定植物材料的校准曲线,有必要收获那些的样品 作物(在照片后立即)具有不同的植物密度或在不同的发育阶段,用于LAI和/或绿色生物质的破坏性实验室测定。 LAI的测定需要用叶面积计测量绿叶的面积(一侧薄层)。根据作物干重(CDW)测定绿色生物质需要在70℃下烘干48小时后,共同称重取样的绿色部分 - 叶,茎和尖峰(如果存在)。在所有情况下,必须测量该图的代表性样品,至少所有植物包含在该图的0.075μm2部分中。在丢弃两个区域之后,可以随机选择代表性样品:具有较高生物量的10%的图和具有较低生物量的图的10%,两者都是视觉估计的。
    例如,在冬季谷物作物中,校准曲线应包含从幼苗(Zadoks阶段12)到开花期(Zadoks阶段65)的点。植物密度必须覆盖使用校准的预期范围 图5显示了在开花前冬季谷物的校准曲线的一个例子,基于Casadesus和Villegas(2014)。从该示例中,将GF转换为LAI的计算为:
    LAI = 3.164×GF-0.143
    CDW = 440.2×GF-56.81


  1. 图3是图片应如何看起来的示例。图4示出了在采样会话中获得的若干图片的样本。图5显示了LAI的校准曲线的一个例子


    图4.在采样会话中获得的图片序列示例。在这种情况下,每个图片有6张图片,每个图片之间有一个附加图片 - 显示了实地试验的透视图 - 帮助识别相邻图像。该图仅示出了样品的一部分;整个会话可以包括几百个图像

    图5.冬季谷物中LAI的一般校准曲线的实例。 GF是从开花前两个不同日期24个地块(包括面包小麦,黑小麦和千日菊)中破坏性地采集LAI之前获得的照片计算的。




  1. Aparicio,N.,Villegas,D.,Casadesús,J.,Araus,J.L.and Royo,C。(2000)。 光谱植被指数作为确定硬粒小麦产量的非破坏性工具。 Agron J 92:83-91
  2. Casadesús,J。和Villegas,D。(2014)。 传统数码相机作为评估谷物育种的叶面积指数和生物量的工具。植物生物学杂志56(1):7-14。
  3. Casadeus,J.,Kaya,Y.,Bort,J.,Nachit,MM,Araus,JL,Amor,S.,Ferrazzano,G.,Maalouf,F.,Maccaferri,M.,Martos,V.,Ouabbou, H.和Villegas,D。(2007)。 使用来自常规数码相机的植被指数作为小麦的选择标准在水有限的环境中繁殖。 Ann Appl Biol 150,227-236
  4. Zadoks,J.C.,Chang,T.T.and Konzak,C.F。(1974)。 谷物生长阶段的十进制代码。 Weed Res 14,415-421。
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引用:Casadesús, J. and Villegas, D. (2015). Simple Digital Photography for Assessing Biomass and Leaf Area Index in Cereals. Bio-protocol 5(11): e1488. DOI: 10.21769/BioProtoc.1488.