The authors of this research would like to thank the field management staff at the experimental stations of Colmenar de Oreja (Aranjuez) of the Instituto Nacional de Investigaci&#243;n y Tecnolog&#237;a Agraria y Alimentaria (INIA) and Zamadue&#241;as (Valladolid) of the Instituto de Tecnolog&#237;a Agraria de Castilla y Le&#243;n (ITACyL) for their field support of the research study crops used. This study was supported by the research project AGL2016-76527-R from MINECO, Spain and part of a collaboration project with Syngenta, Spain. The BPIN 2013000100103 fellowship from the &#34;Formaci&#243;n de Talento Humano de Alto Nivel, Gobernaci&#243;n del Tolima - Universidad del Tolima, Colombia&#34; was the sole funding support for the first author Jose Armando Fernandez-Gallego. The primary funding source of the corresponding author, Shawn C. Kefauver, came from the ICREA Academia program through a grant awarded to Prof. Jose Luis Araus.

1. Prefield crop growth stage and environmental conditions
<ol>
	<li>Make sure that the crop growth stage is approximately between grain filling and near crop maturity, with ears that are still green even if the leaves are senescent (which corresponds in the case of wheat to the range 60-87 of Zadoks&#39; scale16). Some yellowing of the leaves is acceptable but not necessary.</li>
	<li>Prepare a sampling plan for image capture with various replicates (pictures per plot) in order to capture plot/...

<ol>
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A., Kefauver, S. C., Guti&#233;rrez, N. A., Nieto-Taladriz, M. T., Araus, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Wheat+ear+counting+in-field+conditions:+high+throughput+and+low-cost+approach+using+RGB+images.">Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images.</a> Plant Methods. 14 (1), 22 (2018).</li><li>Schneider, C. A., Rasband, W. S., Eliceiri, K. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NIH+Image+to+ImageJ:+25+years+of+image+analysis.">NIH Image to ImageJ: 25 years of image analysis.</a> Nature Methods. 9 (7), 671-675 (2012).</li><li>Zadoks, J., Chang, T., Konzak, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+decimal+growth+code+for+the+growth+stages+of+cereals.">A decimal growth code for the growth stages of cereals.</a> Weed Research. 14 (14), 415-421 (1974).</li><li>Casades&#250;s, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+vegetation+indices+derived+from+conventional+digital+cameras+as+selection+criteria+for+wheat+breeding+in+water-limited+environments.">Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments.</a> Annals of Applied Biology. 150 (2), 227-236 (2007).</li><li>Hunt, E. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+visible+band+index+for+remote+sensing+leaf+chlorophyll+content+at+the+canopy+scale.">A visible band index for remote sensing leaf chlorophyll content at the canopy scale.</a> International Journal of Applied Earth Observation and Geoinformation. 21 (1), 103-112 (2013).</li><li>Zaman-Allah, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unmanned+aerial+platform-based+multi-spectral+imaging+for+field+phenotyping+of+maize.">Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize.</a> Plant Methods. 11 (1), 35 (2015).</li><li>Slafer, G. A., Savin, R., Sadras, V. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coarse+and+fine+regulation+of+wheat+yield+components+in+response+to+genotype+and+environment.">Coarse and fine regulation of wheat yield components in response to genotype and environment.</a> Field Crops Research. 157, 71-83 (2014).</li><li>Liu, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In-field+wheatear+counting+based+on+image+processing+technology.">In-field wheatear counting based on image processing technology.</a> Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery. 45 (2), 282-290 (2014).</li><li>Cointault, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Color+and+Frequential+Proxy-Detection+Image+Processing+for+Crop+Characterization+in+a+Context+of+Precision+Agriculture.">Color and Frequential Proxy-Detection Image Processing for Crop Characterization in a Context of Precision Agriculture.</a> Agricultural Science. , 49-70 (2012).</li><li>Abbad, H., El Jaafari, S., Bort, J., Araus, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparative+relationship+of+the+flag+leaf+and+the+ear+photosynthesis+with+the+biomass+and+grain+yield+of+durum+wheat+under+a+range+of+water+conditions+and+different+genotypes.">Comparative relationship of the flag leaf and the ear photosynthesis with the biomass and grain yield of durum wheat under a range of water conditions and different genotypes.</a> Agronomie. 24, 19-28 (2004).</li><li>Ko, S. J., Lee, Y. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Center+weighted+median+filters+and+their+applications+to+image+enhancement.">Center weighted median filters and their applications to image enhancement.</a> IEEE Transactions on Circuits and Systems. 38 (9), 984-993 (1991).</li><li>Smo&#322;ka, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nonlinear+techniques+of+noise+reduction+in+digital+color+images.">Nonlinear techniques of noise reduction in digital color images.</a> Wydawnictwo Politechniki &#346;l&#261;skiej. , (2004).</li></ol>

The authors have nothing to disclose.

Increased agility, consistency, and precision are key to developing useful new phenotyping tools to assist the crop-breeding community in their efforts to increase grain yield despite negative pressures related to global climate change. Efficient and accurate assessments of cereal ear density, as a major agronomic component of yield of important staple crops, will help provide the tools needed for feeding future generations. Focusing on the improvement and support of crop-breeding efforts in field conditions helps keep t...

The world cereal utilization in 2017/2018 is reported expand by 1% from the previous year1. Based on the latest predictions for cereal production and population utilization, world cereal stocks need to increase yields at a faster rate in order to meet growing demands, while also adapting to increasing effects of climate change2. Therefore, there is an important focus on yield improvement in cereal crops through improved crop breeding techniques. Two the most important and harvested cereals in the Mediterranean region are selected as examples for this study, namely, durum wheat (Triticum aestivum L. ssp. durum [Desf.]) and barley (Hordeum vulgare L.). Durum wheat is, by extension, the most cultivated cereal in the south and east margins of the Mediterranean Basin and is the 10th most important crop worldwide, owing to its annual production of 37 million tons annually3, while barley is the fourth global grain in terms of production, with a global production at 144.6 million tons annually4.
Remote sensing and proximal image analysis techniques are increasingly key tools in the advancement of field high-throughput plant phenotyping (HTPP) as they not only provide more agile but also, often, more precise and consistent retrievals of target crop biophysiological traits, such as assessments of photosynthetic activity and biomass, preharvest yield estimates, and even improvements in trait heritability, such as efficiency in resource use and uptake5,6,7,8,9. Remote sensing has traditionally focused on multispectral, hyperspectral, and thermal imaging sensors from aerial platforms for precision agriculture at the field scale or for plant phenotyping studies at the microplot scale10. Common, commercially available digital cameras that measure only visible reflected light were often overlooked, despite their very high spatial resolution, but have recently become popular as new innovative image-processing algorithms are increasingly able to take advantage of the detailed color and spatial information that they provide. Many of the newest innovations in advanced agricultural image analyses increasingly rely on the interpretation of data provided by very high-resolution (VHR) RGB images (for their measurement of red, green, and blue visible light reflectance), including crop monitoring (vigor, phenology, disease assessments, and identification), segmentation and quantification (emergence, ear density, flower and fruit counts), and even full 3D reconstructions based on a new structure from motion workflows11.
One of the most essential points for improvement in cereal productivity is a more efficient assessment of yield, which is determined by three major components: ear density or the number of ears per square meter (ears/m2), the number of the grains per ear, and the thousand-kernel weight. Ear density can be obtained manually in the field, but this method is laborious, time-consuming, and lacking in a single standardized protocol, which together may result in a significant source of error. Incorporating the automatic counting of ears is a challenging task because of the complex crop structure, close plant spacing, high extent of overlap, background elements, and the presence of awns. Recent work has advanced in this direction by using a black background structure supported by a tripod in order to acquire suitable crop images, demonstrating fairly good results in ear counting12. In this way, excessive sunlight and shadow effects were avoided, but such a structure would be cumbersome and a major limitation in an application to field conditions. Another example is an automatic ear counting algorithm developed using a fully automated phenotyping system with a rigid motorized gantry, which was used with good accuracy for counting ear density in a panel composed of five awnless bread wheat (Triticum aestivum L.) varieties growing under different nitrogen conditions13. Recent work by Fernandez-Gallego14 has optimized this process for quicker and easier data capture, using VHR RGB color images followed by more advanced, yet still fully automated, image analyses. The efficient and high-quality data collection in field conditions emphasizes a simplified standardized protocol for consistency and high data capture throughput, while the image-processing algorithm employs the novel use of Laplacian and frequency domain filters to remove undesired image components before applying a segmentation for counting based on finding local maxima (as opposed to full delineation as in other previous studies, which may result in more errors with overlapping ears).
This work proposes a simple system for the automatic quantification of ear density in field conditions, using images acquired from commercially available digital cameras. This system takes advantage of natural light in field conditions and, therefore, requires consideration of some related environmental factors, such as time of day and cloud cover, but remains, in effect, simple to implement. The system has been demonstrated on examples for durum wheat and barley but should be extendable in application to bread wheat, which, besides exhibiting ears with similar morphology, are frequently awnless, but further experiments would be required in order to confirm this. In the data capture protocol presented here, zenithal images are taken by simply holding the camera by hand or using a monopod for positioning the digital camera above the crop. Validation data can be acquired by counting the ears manually for subplots in the field or during postprocessing, by counting ears in the image itself. The image-processing algorithm is composed of three processes that, first, effectively remove unwanted components of the image in a manner that, then, allows for the subsequent segmentation and counting of the individual wheat ears in the acquired images. First, a Laplacian frequency filter is used in order to detect changes in the different spatial directions of the image using the default ImageJ filter settings without window kernel size adjustments (Find Maxima segmentation technique determines the local peaks after the median spatial filter step, at which stage the pixels related with ears have higher pixel values than soil or leaves. Therefore, Find Maxima is used to segment the high values in the image, and those regions are labeled as ears, which identifies ears while also reducing overlapping ear errors. Analyze Particles is then used on the binary images to count and/or measure parameters from the regions created by the contrast between the white and black surface created by the Find Maxima step. The result is then processed to create a binary image segmentation by analyzing the nearest neighbor pixel variance around each local maximum to identify the wheat ear shapes in the filtered image. Finally, the ear density is counted using Analyze Particles, as implemented in Fiji15. Both Find Maxima and Analyze Particles are standalone functions and available as plugins in Fiji (https://imagej.nih.gov/ij/plugins/index.html). Though not presented specifically in the protocol here, preliminary results presented as supplementary material suggest that this technique may be adaptable to conducting ear count surveys from unmanned aerial vehicles (UAVs), providing that the resolution remains sufficiently high14.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>ILCE-QX1 Camera</td>
			<td>Sony</td>
			<td>WW024382</td>
			<td>Compact large sensor digital camera with 23.2 x 15.4 mm sensor size.</td>
		</tr>
		<tr>
			<td>E-M10 Camera</td>
			<td>Olympus</td>
			<td>E-M10</td>
			<td>Compact large sensor digital camera with 17.3 x 13.0 mm sensor size.</td>
		</tr>
		<tr>
			<td>Multipod Monpod</td>
			<td>Sony</td>
			<td>VCT MP1</td>
			<td>&#34;Phenopole&#34; in the JoVE article</td>
		</tr>
		<tr>
			<td>Computer</td>
			<td>Any PC/Mac/Linux</td>
			<td>--</td>
			<td>Data and image analysis</td>
		</tr>
		<tr>
			<td>ImageJ/FIJI (FIJI is just Image J)</td>
			<td>NIH</td>
			<td>http://fiji.sc</td>
			<td>Plug-in and algorithms for data and image analysis</td>
		</tr>
		<tr>
			<td>Circle/Metal Ring</td>
			<td>Generic</td>
			<td>Generic</td>
			<td>Metal ring for in-field validation</td>
		</tr>
		<tr>
			<td>Crab Pliers Clip</td>
			<td>Newer</td>
			<td>90087340</td>
			<td>Circle support and extension arm</td>
		</tr>
	</tbody>
</table>

cereal crop ear counting in field conditions using zenithal rgb images

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, representing an important agronomic yield component for estimating grain yield. Therefore, a quick, efficient, and standardized technique for assessing ear density would aid in improving agricultural management, providing improvements in preharvest yield predictions, or could even be used as a tool for crop breeding when it has been defined as a trait of importance. Not only are the current techniques for manual ear density assessments laborious and time-consuming, but they are also without any official standardized protocol, whether by linear meter, area quadrant, or an extrapolation based on plant ear density and plant counts postharvest. An automatic ear counting algorithm is presented in detail for estimating ear density with only sunlight illumination in field conditions based on zenithal (nadir) natural color (red, green, and blue [RGB]) digital images, allowing for high-throughput standardized measurements. Different field trials of durum wheat and barley distributed geographically across Spain during the 2014/2015 and 2015/2016 crop seasons in irrigated and rainfed trials were used to provide representative results. The three-phase protocol includes crop growth stage and field condition planning, image capture guidelines, and a computer algorithm of three steps: (i) a Laplacian frequency filter to remove low- and high-frequency artifacts, (ii) a median filter to reduce high noise, and (iii) segmentation and counting using local maxima peaks for the final count. Minor adjustments to the algorithm code must be made corresponding to the camera resolution, focal length, and distance between the camera and the crop canopy. The results demonstrate a high success rate (higher than 90%) and R2 values (of 0.62-0.75) between the algorithm counts and the manual image-based ear counts for both durum wheat and barley.

In Figure 8, the results show the determination coefficient between the ear density (number of ears per square meters) using manual counting and the ear counting algorithm for wheat and barley at three different crop growth stages. The first one is durum wheat with a Zadoks&#39; scale between 61 and 65 (R2 = 0.62). The second one is two-row barley with a Zadoks&#39; scale between 71 and 77 (R2 = 0.75), and the last one ...

Watch this Scientific Journal Video about Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images at JoVE.com

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

We present a protocol for counting durum wheat and barley ears, using natural color (RGB) digital photographs taken in natural sunlight under field conditions. With minimal adjustments for camera parameters and some environmental condition limitations, the technique provides precise and consistent results across a range of growth stages.

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, ...

We would like to show you our approach for cereal ear counting in field conditions. The objective of this study is to demonstrate a quick and efficient way to count wheat ears in field conditions. Durum wheat and barley are by extension the most cultivated cereals in the south and east areas of the Mediterranean basin.In those areas, as a consequence of the climate change, the environmental conditions will be changed. We have to work in order to increase the production. In this sense, remote and proximal sensing images have become an important tool in field high-throughput phenotyping using different kids of sensors.One of the essential points for improving cereal productivity is a more efficient assessment of yield. Determined by the following three yield components:Ear density, the number of grains per ear, and the thousand kernel weight. We will try to develop an automatic counting of ears per unit crop area in other words ear density.The protocol that we've developed is using a 20 megapixel camera taking pictures downward looking or perfectly xenapho or natur at a distance of approximately 80 cm from the top of the crop canopy. For validation purposes, we do both in the field wheat ear counts, or barley ear counts as well as manual counts of the image in order to validate the technique and fine tune the algorithms. It's also important when taking the field photos, that the pictures be captured within two hours of solar noon.This is important because it avoids shadowing effects, that complicate the image analysis in the 2nd phase of the protocol. In this part, we would like to show you our approach for cereal ear counting in field conditions. This work was carried out in collaboration with ITACyL, INIA, and Syngenta.Let's begin. The first step of our protocol is select the appropriate crop growth stage. In our case, we have use the stages between grain filling and near chromatology that correspond in the South East case a number between 60 and 87.In the Figure A:Wheat and Figure B:Barley we chose an example of satellite images of our data set. The image capture has 3 parameters:sensor width, photo lens and distance between the camera and the canopy. With this information we can calculate the square meters of the image.Algorithm implementation and adjustments, these are the pa-blan-y steps. As an input we have an RGB image. Laplacian frequency filter we use it to remove part of the soil, leave and unwanted brightness.The medium filter reviewing the noise, and finally find maxima determine local picks. The output image show the ears detected. Algorithm implementation and adjustments.If the images were taken with different camera specification or distance between the canopy and the camera we can adjust some algorithm parameters. The Laplacian filter is still the same. In the medium filter and find maxima we can change the diameter and the noise parameter.Algorithm validation. For the validation step we have marked each ear in the original image and then the number of marks in the image were counted using a simple algorithm. The results were used to calculate the success rate.We have also included a circle to have a physical reference in the image. Algorithm implementation using images. This is the cereal scanner plug-in.You will find in the central tab data counting. In options you can select the input images and also the place where you are going to save the results. I would like to show you how the macro works using one image.We're going to select this image and now we're going to run the first step. This is the frequency formation of the image and here we have the result. The second step is the medium filter.We're going to run this step, and this is the result. Finally we're going to run the last part of the cut and these are the result, these final numbers is the number of ears detected. The other way to the same is click here, we process, find maxima, and click here.This is another way to use the ear counting algorithm. You can visit our website integrativecropecophysiologygroup. com and here in software development, you will find the cereal scanner.To access permission please write directly to this email. Please follow the steps to install the plug in. With the plug in installed please go directly to plug ins, cereal scanner, open cereal scanner.Now we're going to use two images. Go directly to cereal scanner, ear counting, and in options, select your files. Here you can use the distance between the canopy and the camera.In our case we use it 80 cm. Here you can select the focal length. Finally in the results file you will find the ear counting results and just process.These are our results. Here we have the name of the image and in front we have the number of ears detected. These are the results for Wheat and Barley.For each graphic the X-axis represents the manual counting. Click by click, and the Y-axis represents the algorithm counting. Both axis in a square meter scale.In the fifth graphic for wheat we have obtained a determination coefficient equal to 0.62 and in the second graphic for barley we have obtained a determination coefficient equal to 0.75. In the final image for wheat we have also obtained a determination coefficient equal to 0.75. In counting is one of the most laborious to work and time consuming during variety evaluation cycle and yield prediction.For this reason a fast and friendly technique is needed to improve and expand it's use in precise agricultural and crop breeding and for yield forecasting. This use of our cereal scanner is the fruit of collaboration of Public Provides Corporation with the objective to make ear counting with improved efficiency and time and resources. On the other hand to capture data of high quality to end up with more precise product database.While yet to conclude I would like to stress similar aspects of the mis-a-lory you would have been presented here, one is the local, so this is really a form of technology that doesn't need any kind of complement so people may go to the field with their camera, with a mobile phone, taking images under sunlight and that's all. Second, a very important point, is that this is only an assundun methodology that is really a contrast with the cognancy to an issue of the people counting the number of ears in a very different ways. So the local that has an ex-tres-cent before is important because you may do in few minutes what a couple or three people will need perhaps in several hours.So the counting, the budget is a really important point. Another aspect is that the methodology is minimal for decreasing resolution. That means that you may mount the RGB camera in a megaplant in a kind of automatic plant-form and then you may do an automatic counting on this facts.And finally I am saying this is not the end of the way so we have really a percentage methodology that may improve in the future through the combination with all the approaches like for example the use of different RGB color and spaces for example in combination with multi-spectral imagining or even the use of thermal or thermal images. And that's all, thank you.

cereal-crop-ear-counting-in-field-conditions-using-zenithal-rgb-images

Research

JoVE Journal

Environment

9.1K Görüntüleme.  University of Barcelona. Biz alan koşullarında tahıl kulak sayma için yaklaşımımızı göstermek istiyorum. Bu çalışmanın amacı, tarla koşullarında buğday kulaklarını saymak için hızlı ve etkili bir yol göstermektir. Durum buğday ve arpa akdeniz havzasının güney ve doğu bölgelerinde en çok yetiştirilen tahıllar uzantısı olarak vardır.Bu alanlarda, iklim değişikliğinin bir sonucu olarak, çevre koşulları değişecektir. Üretimi artırmak için çalışmalıyız. Bu anlamda...