INTERACCIÓN ENTRE ALUMNOS

RGB images” was published in the Journal European Journal of Agronomy with an impact factor of 4.13 in 2019, is a journal placed in the first decile of the science areas: agricultural and biological sciences - agronomy and crop science, agricultural and biological sciences - plant science, and agricultural and biological sciences - soil science. In this study, we have developed new the u*v*A index to estimate the photosynthetic area of the canopy using high-resolution images. The results demonstrate the best phenotypic predictions of grain yield during the last part of the crop cycle for irrigated and late planting trial and during the middle part of the crop cycle for rainfed conditions. The heritability and genetic correlation demonstrated the capacity of the RGB indexes to serve as an indirect selection tool for assessing grain yield. Overall the study highlights the capability of an affordable approach based in the acquisition of RGB images at the plot level for crop phenotyping. The doctoral student has managed the field data collection and developed the algorithms, light concept studies, statistical analysis, validation

Proceedings papers in conference that include oral presentation:

- Conference SPIE Remote Sensing 2018.

(Best student paper of the conference)

Fernandez-Gallego, J.A., Kefauver, S.C., Gutiérrez, N.A., Nieto-Taladriz, M.T., Araus, J.L., 2018. Automatic wheat ear counting in-field conditions: simulation and implication of lower resolution images, in: Neale, C.M., Maltese, A. (Eds.), Proc. SPIE 10783, Remote Sensing for Agriculture,

Ecosystems, and Hydrology XX, 107830M. SPIE, p. 23.

doi:10.1117/12.2500083

- Conference SPIE Remote Sensing 2018.

Fernandez-Gallego, J.A., Kefauver, S.C., Kerfal, S., Araus, J.L., 2018. Comparative canopy cover estimation using RGB images from UAV and ground, in: Proc. SPIE 10783, Remote Sensing for Agriculture, Ecosystems,

and Hydrology XX, 107830M. p. 20. doi:10.1117/12.2501531

- Conference SPIE Remote Sensing 2019.

Fernandez-Gallego, J.A., Buchaillot M., Gutiérrez, N.A., Nieto-Taladriz, M.T.,

Araus, J.L., Kefauver, S.C., 2019. Wheat ear temperature estimation using a thermal radiometric camera, in: Proc. SPIE 11149, Remote Sensing for

Other articles where the doctoral student participated as a co-author:

- Frontiers in Plant Science. Impact factor 4.14 in 2017.

Kefauver, S.C., Vicente, R., Vergara-Díaz, O., Fernandez-Gallego, J.A., Kerfal, S., Lopez, A., Melichar, J.P.E., Serret Molins, M.D., Araus, J.L., 2017. Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley. Front. Plant Sci. 8, 1–15. doi:10.3389/fpls.2017.01733

- Journal of Experimental Botany. Impact factor 5.47 in 2018.

Vergara-Díaz, O., Chairi, F., Vicente, R., Fernandez-Gallego, J.A., Nieto- Taladriz, M.T., Aparicio, N., Kefauver, S.C., Araus, J.L., 2018. Leaf dorsoventrality as a paramount factor determining spectral performance in field-grown wheat under contrasting water regimes. J. Exp. Bot. 69, 3081- 3094. doi:10.1093/jxb/ery109.

- Remote Sensing. Impact factor 4.12 in 2019.

Sancho-Adamson, M., Trillas, M.I., Bort, J., Fernandez-Gallego, J.A., Romanyà, J., 2019. Use of RGB Vegetation Indexes in Assessing Early Effects of Verticillium Wilt of Olive in Asymptomatic Plants in High and Low Fertility Scenarios. Remote Sensing 11, 607 doi:10.3390/rs11060607

Phenotyping: A Proof of Concept with Durum Wheat. Remote Sens. 11, 1244. doi:10.3390/rs11101244.

Internships:

- Wheat ear counting using dron images. Research Institute for Agriculture, Fisheries and Food (ILVO). Melle, Belgium, 2018.

- Tree crown segmentation (Pinus pinea L.) using RGB and multispectral imagery. Centre de Ciència i Tecnologia Forestal de Catalunya (CTFC). Solsona, Spain, 2019.

Training courses, courses and workshops:

- Workshop. Computer vision problems in plant phenotyping (CVPPP). Venice, Italia, 2017.

- Course. Breeding small grain cereal crops in a climate change scenario. IAMZ-CIHEAM. Zaragoza, Spain, 2018.

- Workshop. Field Phenomics. EMPHASIS. Melle, Belgium, 2018.

- Training course. Wheat Phenotyping using UAVs. Wheat Initiative. Saskatoon, Canada, 2019.

Symposiums that include oral presentation:

- I Spanish symposium on physiology and cereal breeding. Zaragoza, Spain, 2018.

- II Spanish Symposium on Physiology and Cereal Breeding. Cordoba, Spain, 2019.

To certify this for corresponding purposes,

3.1. Chapter 1

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

Jose A. Fernandez-Gallego1_{, Shawn C. Kefauver}1_{, Nieves Aparicio Gutiérrez}2_,

María Teresa Nieto-Taladriz3 _{and José Luis Araus}1

1 _{Plant Physiology Section, Department of Evolutionary Biology, Ecology and}

Environmental Sciences, Faculty of Biology, University of Barcelona, Diagonal 643, 08028 Barcelona, Spain.

Published in:

ABSTRACT

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: (i) a Laplacian frequency filter chosen to remove low and high frequency elements appearing in an image, (ii) a Median filter to reduce high noise still present around the ears and (iii) segmentation using Find Maxima to segment local peaks and determine the ear count within the image. 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 was 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

Developing robust, low-cost and efficient field methods to assess wheat ear density, as a major agronomic 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.

Fernandez-Gallego et al. Plant Methods (2018) 14:22 https://doi.org/10.1186/s13007-018-0289-4