Name: cereal crop ear counting in field conditions using zenithal rgb images
Uploaded: 2019-02-02T12:30:00
Description: Watch this Scientific Journal Video about Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images at JoVE.com

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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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, ...

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