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The productivity of wheat in the Mediterranean region is under threat due to climate-change-related environmental factors, including fungal diseases that can negatively impact wheat yield and quality. Wheat phenotyping tools utilizing affordable, high-throughput plant phenotyping (HTPP) techniques, such as aerial and ground RGB images and quick canopy and leaf sensors, can aid in assessing crop status and selecting tolerant wheat varieties. This study focused on the impact of fungal diseases on wheat productivity in the Mediterranean region, considering the need for a precise selection of tolerant wheat varieties. This research examined the use of affordable HTPP methods, including imaging and active multispectral sensors, to aid in crop management for improved wheat health and to support commercial field phenotyping programs. This study evaluated 40 advanced lines of bread wheat (Triticum aestivum L.) at five locations across northern Spain, comparing fungicide-treated and untreated blocks under fungal disease pressure (Septoria, brown rust, and stripe rust observed). Measurements of leaf-level pigments and canopy vegetation indexes were taken using portable sensors, field cameras, and imaging sensors mounted on unmanned aerial vehicles (UAVs). Significant differences were observed in Dualex flavonoids and the nitrogen balance index (NBI) between treatments in some locations (p < 0.001 between Elorz and Ejea). Measurements of canopy vigor and color at the plot level showed significant differences between treatments at all sites, highlighting indexes such as the green area (GA), crop senescence index (CSI), and triangular greenness index (TGI) in assessing the effects of fungicide treatments on different wheat cultivars. RGB vegetation indexes from the ground and UAV were highly correlated (r = 0.817 and r = 0.810 for TGI and NGRDI). However, the Greenseeker NDVI sensor was found to be more effective in estimating grain yield and protein content (R2 = 0.61–0.7 and R2 = 0.45–0.55, respectively) compared to the aerial AgroCam GEO NDVI (R2 = 0.25–0.35 and R2 = 0.12–0.21, respectively). We suggest as a practical consideration the use of the GreenSeeker NDVI as more user-friendly and less affected by external environmental factors. This study emphasized the throughput benefits of RGB UAV HTPPs with the high similarity between ground and aerial results and highlighted the potential for HTPPs in supporting the selection of fungal-disease-resistant bread wheat varieties.

Wheat grain quality characteristics have experienced increasing attention as a central factor affecting wheat end-use products quality and human health. Nonetheless, in the last decades a reduction in grain quality has been observed. Therefore, it is central to develop efficient quality-related phenotyping tools. In this sense, one of the most relevant wheat features related to grain quality traits is grain nitrogen content, which is directly linked to grain protein content and monitorable with remote sensing approaches. Moreover, the relation between nitrogen fertilization and grain nitrogen content (protein) plays a central role in the sustainability of agriculture. Both aiming to develop efficient phenotyping tools using remote sensing instruments and to advance towards a field-level efficient and sustainable monitoring of grain nitrogen status, this paper studies the efficacy of various sensors, multispectral and visible red–greenblue (RGB), at different scales, ground and unmanned aerial vehicle (UAV), and phenological stages (anthesis and grain filling) to estimate grain nitrogen content. Linear models were calculated using vegetation indices at each sensing level, sensor type and phenological stage. Furthermore, this study explores the up-scalability of the best performing model to satellite level Sentinel-2 equivalent data. We found that models built at the phenological stage of anthesis with UAV-level multispectral cameras using red-edge bands outperformed grain nitrogen content estimation (R2 = 0.42, RMSE = 0.18%) in comparison with those models built with RGB imagery at ground and aerial level, as well as with those built with widely used ground-level multispectral sensors. We also demonstrated the possibility to use UAV-built multispectral linear models at the satellite scale to determine grain nitrogen content effectively (R2 = 0.40, RMSE = 0.29%) at actual wheat fields. This study was supported by the projects PID2019-106650RB-C21 (Ministerio de Ciencia e Innovación, MICINN, Spain) and 0011-1365-2018-000213/0011-1365-2018-000150 (Government of Navarre, Spain). J.S. is recipient of a FPI doctoral fellowship (Grant: PRE2020-091907) from MICINN, Spain. J.L.A. acknowledges support from ICREA Academia, Generalitat de Catalunya, Spain. S.C.K. is supported by the Ramon y Cajal RYC-2019-027818-I research fellowship from MICINN, Spain. The processing of satellite images was supported by the European Cooperation in Science and Technology Action CA17134 SENSECO.

High-throughput crop phenotyping, particularly under field conditions, is nowadays perceived as a key factor limiting crop genetic advance. Phenotyping not only facilitates conventional breeding, but it is necessary to fully exploit the capabilities of molecular breeding, and it can be exploited to predict breeding targets for the years ahead at the regional level through more advanced simulation models and decision support systems. In terms of phenotyping, it is necessary to determined which selection traits are relevant in each situation, and which phenotyping tools/methods are available to assess such traits. Remote sensing methodologies are currently the most popular approaches, even when lab-based analyses are still relevant in many circumstances. On top of that, data processing and automation, together with machine learning/deep learning are contributing to the wide range of applications for phenotyping. This review addresses spectral and RGB sensing as the most popular remote sensing approaches, alongside stable isotope composition as an example of a lab-based tool, and root phenotyping, which represents one of the frontiers for field phenotyping. Further, we consider the two most promising forms of aerial platforms (UAV and satellites) and some of the emerging data-processing techniques. The review includes three Boxes that examine specific case studies. This article is protected by copyright. All rights reserved.

15 Pags., Ideotypic characteristics of durum wheat associated with higher yield under different water and temperature regimes were studied under Mediterranean conditions. Six semi-dwarf cultivars with contrasting agronomic performance were grown during two consecutive years under winter-planted rainfed and winter-planted support-irrigation conditions and a late-planting trial under support irrigation, at the INIA station of Colmenar de Oreja (Madrid). Different traits were assessed to inform on: water status, root performance, phenology, photosynthetic capacity, crop growth, grain yield and agronomic yield components. Under support irrigation and normal planting, genotypes with higher grain yield exhibited better water status (lower δ13C and canopy temperature), assimilation of more superficial water (higher δ18O), earlier heading and greater plant height and ear density. Under water-limited conditions (rainfed), the best genotypes also exhibited better water status (lower δ13C) and earlier heading, but higher specific root length with extraction of water from deeper soil layers (lower δ18O), more efficient N metabolism (higher δ15N and NBI) and consequently stronger growth (plant height and NDVI), and greater ear density and thousand grain weight. Under warmer conditions (late planting), the best genotypes also exhibited better water status (lower δ13C) and greater plant height and photoprotective mechanisms (higher flavonoid content and lower chlorophyll content). However, the strong differences in drought between consecutive years determined other specific ideotypic traits within each of the three growing conditions and the particular year. Our study suggests specific ideotypes when breeding durum wheat under different agronomic scenarios, but also stresses that interannual variation in water conditions, typical of Mediterranean conditions, should be taken into account., This study was supported by the Spanish Projects PID2019-106650RB-C21 and PCIN-2017-063, Ministerio de Ciencia e Innovacion, ´Spain. FZR is a recipient of a research grant (FI-AGAUR) sponsored by the Agency for Management of University and Research Grants (AGAUR), in collaboration with the University of Barcelona. SCK is supported by the Ramon y Cajal RYC-2019-027818-I research fellowship from the Ministerio de Ciencia e Innovacion, ´ Spain. JLA acknowledges support from the Catalan Institution for Research and Advanced Studies, Generalitat de Catalunya, Sains, through its program ICREA Academia.

Due to the multi-target effect of biostimulants, an efficient, low-cost, and rapid approach for tracking their mode of action is required. In this paper, the effect of seed coating with thyme essential oil and the bacterium Paraburkholderia phytofirmans on plant growth as well as water stress tolerance was investigated using MultispeQ device in wheat seedling grown under controlled conditions. Both treatments enhanced photosynthesis efficiency and tolerance to water stress through reducing plant damage. Plants coated with P. phytofirmans tend to maintain photosynthesis rate, while plants coated with thyme oil tend to maintain water content and reduce energy-demanding processes. All results correlated with previous biochemical, isotopic, and molecular analysis results highlighting MultispeQ reliability.

12 Pág. Dirección Técnica de Evaluación de Variedades y Laboratorios (DTEVL), Durum wheat is an important cereal that is widely grown in the Mediterranean basin. In addition to high yield, grain quality traits are of high importance for farmers. The strong influence of climatic conditions makes the improvement of grain quality traits, like protein content, vitreousness, and test weight, a challenging task. Evaluation of quality traits post-harvest is time- and labor-intensive and requires expensive equipment, such as near-infrared spectroscopes or hyperspectral imagers. Predicting not only yield but also important quality traits in the field before harvest is of high value for breeders aiming to optimize resource allocation. Implementation of efficient approaches for trait prediction, such as the use of high-resolution spectral data acquired by a multispectral camera mounted on unmanned aerial vehicles (UAVs), needs to be explored. In this study, we have acquired multispectral image data with an 11-band multispectral camera mounted on a UAV and analyzed the data with machine learning (ML) models to predict grain yield and important quality traits in breeding micro-plots. Combining 11-band multispectral data for 34 cultivars and 16 environments allowed to develop ML models with good prediction capability. Applying the trained models to test sets explained a considerable degree of phenotypic variance with good accuracy showing r squared values of 0.84, 0.69, 0.64, and 0.61 and normalized root mean squared errors of 0.17, 0.07, 0.14, and 0.03 for grain yield, protein content, vitreousness, and test weight, respectively., This study was supported in part by the Spanish project PID2019-106650RB-C21 from the Ministerio de Ciencia e Innovación. TV is recipient of a Juan de la Cierva-Formación post-doctoral contract, from the Ministerio de Ciencia e Innovación, Spain. SK is supported by the Ramon y Cajal RYC-2019-027818-I research fellowship from the Ministerio de Ciencia e Innovación, Spain. JLA also acknowledges the support of the Catalan Institution for Research and Advanced Studies (ICREA, Generalitat de Catalunya, Spain), through the ICREA Academia Program.

Seed priming through seed coating with natural biostimulants is a promising approach to enhance plant tolerance to biotic and abiotic stresses. As a result of the wide range of investigated biostimulants and their multitarget effect in plants, high-throughput phenotyping is fundamentally important. An efficient, low-cost and rapid approach for tracking the mode of action of biostimulants is needed. In this work, the effect of seed coating with thyme essential oil and the bacterium Paraburkholderia phytofirmans on plant growth promotion, photosynthetic state and water–nutrient stress tolerance was investigated using the MultispeQ device and the biochemical quantification of abscisic acid (ABA) in wheat seedlings grown under controlled conditions. The MultispeQ device was used to estimate multiple non-invasive measures such as the leaf temperature and thickness, chlorophyll and the photosynthetic state (defined using the standard parameters LEF, qL, ECSt, RFd, Phi2, Fv′/Fm′, NPQt, PhiNPQ, PhiNO). Both treatments enhanced the tolerance to stress evidenced by an enhanced shoot and roots dry matter and photoprotective processes. P. phytofirmans tends to induce lateral root development and enhance photosynthesis. In contrast, thyme oil tends to induce root elongation, water storage capacity and reduce the energy-demanding photosynthetic processes. These results correlated with the endogenous ABA content, used as a reference, highlighting the reliability of the MultispeQ device. This study promotes the importance of phenotyping with the MultispeQ device for achieving better understanding without making invasive measurements.

Enhancing nitrogen fertilization efficiency for improving yield is a major challenge for smallholder farming systems. Rapid and cost-effective methodologies with the capability to assess the effects of fertilization are required to facilitate smallholder farm management. This study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe. Among the measurements made on individual leaves, chlorophyll readings were the best indicators for both N content in leaves (R

The state-of-the-art on the use of commercially available consumer color digital cameras, which capture Red, Green and Blue light covering the visible spectrum with broad spectral bands but at high spatial resolution and with accurate color calibration has produced some interesting results in recent years, bringing back the benefits of “hyperspatial” imaging for estimating various plant physiological characteristics related to both biotic and abiotic stressors. Here we will review various RGB vegetation indexes that use the spectral concept for the estimation of biomass and canopy chlorophyll, the Normalized Green Red Difference Index (NGRDI) and the Triangular Greenness Index (TGI), as well as others that are in popular use based on this same concept as more traditional style vegetation indices often used with multispectral data. We will also introduce spectral indexes based on alternate color space transforms such as Hue Saturation Intensity (HSI), CIE-Lab and CIE-Luv and their practical calculations. Practical aspects of the calculation of these RGB vegetation indexes are offered using open-source software plugins for FIJI (FIJI is Just ImageJ), including the MaizeScanner, CerealScanner, and their mobile-to-cloud ODK (Open Data Kit) versions Fusion and CerealsFusion.

In spite of the benefits of heterosis in maize breeding, little is known about the physiological mechanisms of this phenomenon and its genetic control under different water regimes. This study aimed to understand the heterosis effects on plant growth, the photosynthetic and transpiration traits, and the root traits of four inbred popcorn lines and their hybrids, including their reciprocal combinations. Plants were grown in lysimeters, inside a rain shelter, under two water conditions (water stress - WS; well-watered - WW) until anthesis. Plant growth traits included shoot biomass, plant height, and leaf area. Photosynthetic traits comprised leaf pigment and total nitrogen content, chlorophyll fluorescence, gas exchange, water use efficiency and stomatal index and density, along with the stable carbon (δ13C) and nitrogen (δ15N) isotope compositions of the last developed leaf. Root weight density and specific root length were also recorded. Greater heterosis effects were observed for traits related to plant growth and root weight density, and specifically under WS. Traits related to root weight density in deeper soil layers benefited markedly from heterosis, but there were no advantages in terms of stomatal conductance and water status in general. Apparently, only δ13C supported a better water status under WS, and was observed in the hybrids in particular. Non-additive gene effects were predominant in controlling of most of the growth and root traits studied, supporting the conclusion that the heterosis effect is especially favorable under water-limiting conditions. Moreover, the choice of the female parent is essential for traits related to gas exchange when breeding for better resilience to drought.

Coating seeds with biostimulants is among the promising approaches in crop production to increase crop tolerance to drought stress. In this study, we evaluated the potential of coating durum wheat seeds of the cultivar &lsquo, Karim&rsquo, with thyme essential oil on enhancing seed germination and seedling growth, and on plant growth promotion and induction of drought resistance. Coated seeds were pre-germinated, grown in hydroponics, and grown in pots under controlled well-watered and progressive water/nutrient stress conditions. Seed coating with thyme oil increased germination rate and enhanced seedling growth development in hydroponics. In the pot experiment, thyme oil increased, when well watered, root and shoot development, chlorophyll, nitrogen balance index (NBI), abscisic acid (ABA), anthocyanins and flavonoids in leaves, decreased nitrogen isotope composition (&delta, 15N) and increased carbon isotope composition (&delta, 13C) of shoots. Increasing water/nutrient stress in control plants induced higher accumulation of ABA and anthocyanins coupled with a transient decrease in chlorophyll and NBI, a decrease in shoot and root development, the Normalized Difference Vegetation Index (NDVI), shoot C content, &delta, 15N, and an increase in &delta, 13C, revealing the avoidance strategy adopted by the cultivar. Thyme oil had the potential to enhance the avoidance strategy by inducing roots elongation, reducing the loss of shoot and roots dry matter and chlorophyll, maintaining balanced NBI, an decreasing anthocyanins, flavonoids, and &delta, 13C via maintaining lower ABA-mediated-stomatal closure. Thyme oil increased shoot N content and &delta, 15N indicating preferential uptake of the 15N enriched NH4+. Coating seeds with thyme oil is suggested as a promising alternative approach to improve plant&rsquo, s water and nutrient status and to enhance drought resistance.

Proximal remote sensing devices are becoming widely applied in field plant research to estimate biochemical (e.g. pigments or nitrogen) or agronomical (e.g. leaf area, biomass or yield) parameters as indicators of stress. Non-invasive characterization of plant responses allows screening larger populations faster than the conventional procedures that, besides being time-consuming, also implies the destruction of material or is subjective (e.g. visual ranking). This study deals with the comparison of a set of remote sensing devices at single-leaf and whole-canopy levels based on their performance in assessing how the eggplant and its yield respond to crafting as a way to tolerate root-knot nematodes. The results showed that whole-canopy measurements, as the Green Area (GA) derived from the RGB images (r=0.706 and p-value=0.001**) or the canopy temperature (r=-0.619 and p-value=0.005**), outperformed the single-leaf measurements, as the leaf chlorophyll content measured by the Dualex (r=0.422 and p-value=0.059) assessing yield. Moreover, other parameters as the time required to measure each sample or the cost of the sensors were taken into account in the discussion. Everything considered, indexes derived from the RGB images have demonstrated their robust potential for the assessment of crop status, being a low-cost alternative to other more expensive and time-consuming devices.

Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly. However, ground evaluations may still be an alternative in terms of cost and resolution. We compared the performance of red–green–blue (RGB), multispectral, and thermal data of individual plots captured from the ground and taken from a UAV, to assess genotypic differences in yield. Our results showed that crop vigor, together with the quantity and duration of green biomass that contributed to grain filling, were critical phenotypic traits for the selection of germplasm that is better adapted to present and future Mediterranean conditions. In this sense, the use of RGB images is presented as a powerful and low-cost approach for assessing crop performance. For example, broad sense heritability for some RGB indices was clearly higher than that of grain yield in the support irrigation (four times), rainfed (by 50%), and late planting (10%). Moreover, there wasn’t any significant effect from platform proximity (distance between the sensor and crop canopy) on the vegetation indexes, and both ground and aerial measurements performed similarly in assessing yield. This study was supported by the Spanish project AGL2016-76527-R “Fenotipeado En Trigo Duro: Bases Fisiológicas, Criterios De Selección Y Plataformas De Evaluación”, from the Ministerio Economía y Competitividad of the Spanish Government. A.G.-R. is a recipient of a FPI doctoral fellowship from the same institution. We also acknowledge the support from the Institut de Recerca de l’Aigua and the Universitat de Barcelona. J.L.A. acknowledges the funding support from ICREA, Generalitat de Catalunya, Spain.

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.

Maize is the most commonly cultivated cereal in Africa in terms of land area and production. Low yields in this region are very often associated with issues related to low Nitrogen (N), such as low soil fertility or low fertilizer availability. Developing new maize varieties with high and reliable yields in actual field conditions using traditional crop breeding techniques can be slow and costly. Remote sensing has become an important tool in the modernization of field-based High Throughput Plant Phenotyping (HTPP), providing faster gains towards improved yield potential, adaptation to abiotic (water stress, extreme temperatures, and salinity) and biotic (susceptibility to pests and diseases) limiting conditions, and even quality traits. We evaluated the performance of a set of remote sensing indices derived from Red-Green-Blue (RGB) images and the performance of the field-based Normalized Difference Vegetation Index (NDVI) and SPAD as phenotypic traits and crop monitoring tools for assessing maize performance under managed low nitrogen conditions. Phenotyping measurements were conducted on maize plants at two different levels: on the ground and from an airborne UAV (Unmanned Aerial Vehicle) platform. For the RGB indices assessed at the ground level, the strongest correlations compared to yield were observed with Hue, GGA (Greener Green Area), and GA (Green Area) at the ground level, while GGA and CSI (Crop Senescence Index) were better correlated with grain yield at the aerial level. Regarding the field sensors, SPAD exhibited the closest correlation with grain yield, with a higher correlation when measured closer to anthesis. Additionally, we evaluated how these different HTPP data contributed to the improvement of multivariate estimations of crop yield in combination with traditional agronomic field data, such as ASI (Anthesis Silking Data), AD (Anthesis Data), and Plant Height (PH). All multivariate regression models with an R2 higher than 0.50 included one or more of these three agronomic parameters as predictive parameters, but with RGB indices at both levels increased to R2 over 0.60. As such, this research suggests that traditional agronomic data provide information related to grain yield in abiotic stress conditions, but that they may be potentially supplemented by RGB indices from either ground or UAV phenotyping platforms. Finally, in comparison to the same panel of maize varieties grown under optimal conditions, only 11% of the varieties that were in the highest yield-producing quartile under optimal N conditions remained in the highest quartile when grown under managed low N conditions, suggesting that specific breeding for low N tolerance can still produce gains, but that low N productivity is also not necessarily exclusive of high productivity in optimal conditions.

Low soil fertility is one of the factors most limiting agricultural production, with phosphorus deficiency being among the main factors, particularly in developing countries. To deal with such environmental constraints, remote sensing measurements can be used to rapidly assess crop performance and to phenotype a large number of plots in a rapid and cost-effective way. We evaluated the performance of a set of remote sensing indices derived from Red-Green-Blue (RGB) images and multispectral (visible and infrared) data as phenotypic traits and crop monitoring tools for early assessment of maize performance under phosphorus fertilization. Thus, a set of 26 maize hybrids grown under field conditions in Zimbabwe was assayed under contrasting phosphorus fertilization conditions. Remote sensing measurements were conducted in seedlings at two different levels: at the ground and from an aerial platform. Within a particular phosphorus level, some of the RGB indices strongly correlated with grain yield. In general, RGB indices assessed at both ground and aerial levels correlated in a comparable way with grain yield except for indices a* and u*, which correlated better when assessed at the aerial level than at ground level and Greener Area (GGA) which had the opposite correlation. The Normalized Difference Vegetation Index (NDVI) evaluated at ground level with an active sensor also correlated better with grain yield than the NDVI derived from the multispectral camera mounted in the aerial platform. Other multispectral indices like the Soil Adjusted Vegetation Index (SAVI) performed very similarly to NDVI assessed at the aerial level but overall, they correlated in a weaker manner with grain yield than the best RGB indices. This study clearly illustrates the advantage of RGB-derived indices over the more costly and time-consuming multispectral indices. Moreover, the indices best correlated with GY were in general those best correlated with leaf phosphorous content. However, these correlations were clearly weaker than against grain yield and only under low phosphorous conditions. This work reinforces the effectiveness of canopy remote sensing for plant phenotyping and crop management of maize under different phosphorus nutrient conditions and suggests that the RGB indices are the best option.

Vegetation indices and canopy temperature are the most usual remote sensing approaches to assess cereal performance. Understanding the relationships of these parameters and yield may help design more e cient strategies to monitor crop performance. We present an evaluation of vegetation indices (derived from RGB images and multispectral data) and water status traits (through the canopy temperature, stomatal conductance and carbon isotopic composition) measured during the reproductive stage for genotype phenotyping in a study of four wheat genotypes growing under di erent water and nitrogen regimes in north Algeria. Di erences among the cultivars were reported through the vegetation indices, but not with the water status traits. Both approximations correlated significantly with grain yield (GY), reporting stronger correlations under support irrigation and N-fertilization than the rainfed or the no N-fertilization conditions. For N-fertilized trials (irrigated or rainfed) water status parameters were the main factors predicting relative GY performance, while in the absence of N-fertilization, the green canopy area (assessed through GGA) was the main factor negatively correlated with GY. Regression models for GY estimation were generated using data from three consecutive growing seasons. The results highlighted the usefulness of vegetation indices derived from RGB images predicting GY. This study was supported in part by the European project ACLIMAS (EuropeAid/131046/C/ACT/Multi) and the Spanish MINECO project grant No. AGL2016-76527-R).