Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Summary

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.

Abstract

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.

Introduction

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. duru....

Protocol

1. Prefield crop growth stage and environmental conditions

1. 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' scale¹⁶). Some yellowing of the leaves is acceptable but not necessary.
2. Prepare a sampling plan for image capture with various replicates (pictures per plot) in order to capture plot/area variability; the image-processing algorithm will count the number of ears in the image and convert that to ears per square meter (ears/m²

Results

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' scale between 61 and 65 (R² = 0.62). The second one is two-row barley with a Zadoks' scale between 71 and 77 (R² = 0.75), and the last one .......

Discussion

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.......

Materials

Name	Company	Catalog Number	Comments
ILCE-QX1 Camera	Sony	WW024382	Compact large sensor digital camera with 23.2 x 15.4 mm sensor size.
E-M10 Camera	Olympus	E-M10	Compact large sensor digital camera with 17.3 x 13.0 mm sensor size.
Multipod Monpod	Sony	VCT MP1	"Phenopole" in the JoVE article
Computer	Any PC/Mac/Linux	--	Data and image analysis
ImageJ/FIJI (FIJI is just Image J)	NIH	http://fiji.sc	Plug-in and algorithms for data and image analysis
Circle/Metal Ring	Generic	Generic	Metal ring for in-field validation
Crab Pliers Clip	Newer	90087340	Circle support and extension arm