A new article in the European Journal of Agronomy from Jose A Fernandez-Gallego (2019)

Low-cost assessment of grain yield in durum wheat using RGB images

Jose A. Fernandez-Gallego, Shawn C. Kefauver*, Thomas Vatter, Nieves Aparicio Gutiérrez, María Teresa Nieto-Taladriz, José Luis Araus*


The pattern of photosynthetic area of the canopy throughout the crop cycle is an important factor for determining grain yield in wheat. This work proposes the use of zenithal RGB images of the canopy taken in natural light conditions to derive vegetation indices as a low-cost approach to predict grain yield. A set of 23 varieties of durum wheat was monitored in three growing conditions (support irrigation, rainfed and late planting) and two sites (Aranjuez and Valladolid, Spain), totaling 6 field trials. For each plot, digital RGB images were taken periodically from seedling emergence to late grain filling. RGB-based Green Area (GA), Greener Area (GGA), Normalized Green Red Difference Index (NGRDI), Triangular Greenness Index (TGI) and a novel photosynthetic area index based on the CIE L*u*v* colour space (u*v*A) were compared to handheld spectroradiometer Normalised Difference Vegetation Index (NDVI) for reference. In the case of the irrigated and late planting trials, the best phenotypic predictions of grain yield were achieved with the vegetation indices measured during the last part of the crop cycle (i.e. grain filling). For the rainfed trials, the best phenotypic predictions were achieved with indices measured earlier (around heading). Among all the evaluated indices, the novel index performed the best. Considering the heritabilities of the evaluated RGB indices and their genetic correlations with grain yield, index-based predictions of grain yield were best in the early crop stages for both rainfed and irrigated conditions, while for late planting indices measured at different crop stages performed equally well.

Available From:

URL: https://authors.elsevier.com/c/1Yf-C47-DZnchx 

DOI: 10.1016/j.eja.2019.02.007



New Article in JoVE (https://www.jove.com/)

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

José A. Fernandez-Gallego, María Luisa Buchaillot, Adrian Gracia-Romero, Thomas Vatter, Omar Vergara Diaz, Nieves Aparicio Gutiérrez, María Teresa Nieto-Taladriz, Samir Kerfal, Maria Dolors Serre, José Luis Araus, Shawn C. Kefauver*


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 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.


Available from:

URL: https://www.jove.com/video/58695
DOI: doi:10.3791/58695

An article in Plant Methods from Jose A Fernandez-Gallego (2018)

Wheat ear counting in-field conditions: High throughput and low-cost approach using RGB images.

Jose A. Fernandez‑Gallego, Shawn C. Kefauver1* , Nieves Aparicio Gutiérrez, María Teresa Nieto‑Taladriz and José Luis Araus.


Background: The number of ears per unit ground area (ear density) is one of the main agronomic yield components in determining grain yield in wheat. A fast evaluation of this attribute may contribute to monitoring the efficiency of crop management practices, to an early prediction of grain yield or as a phenotyping trait in breeding programs. Currently, the number of ears is counted manually, which is time-consuming. Moreover, there is no single standardized protocol for counting the ears. An automatic ear‑counting algorithm is proposed to estimate ear density under field conditions based on zenithal color digital images taken from above the crop in natural light conditions. Field trials were carried out at two sites in Spain during the 2014/2015 crop season on a set of 24 varieties of durum wheat with two growing conditions per site. The algorithm for counting uses three steps: (1) a Laplacian frequency filter chosen to remove low and high-frequency elements appearing in an image, (2) a Median filter to reduce high noise still present around the ears and (3) segmentation using Find Maxima to segment local peaks and determine the ear count within the image.

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Results: The results demonstrate high success rate (higher than 90%) between the algorithm counts and the manual (image‑based) ear counts, and precision, with a low standard deviation (around 5%). The relationships between algorithm ear counts and grain yield were also significant and greater than the correlation with manual (field‑based) ear counts. In this approach, results demonstrate that automatic ear counting performed on data captured around anthesis correlated better with grain yield than with images captured at later stages when the low performance of ear counting at late grain filling stages was associated with the loss of contrast between canopy and ears.

Conclusions: Developing robust, low‑cost and efficient field methods to assess wheat ear density, as a major agro‑ nomic component of yield, is highly relevant for phenotyping efforts towards increases in grain yield. Although the phenological stage of measurements is important, the robust image analysis algorithm presented here appears to be amenable from aerial or other automated platforms.

Keywords: Digital image processing, Ear counting, Field phenotyping, Laplacian frequency filter, Median filter, Find maxima, Wheat

Available from:

DOI: 10.1186/s13007-018-0289-4

Research Gate:

https://www.researchgate.net/publication/323869100_Wheat_ear_counting_in-field_conditions_High_throughput_and_low cost_approach_using_RGB_images