Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions

Abstract

The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010-2013 a new strain of this pathogen (Warrior/Ambition), against which the present cultivated wheat varieties have no resistance, appeared and spread rapidly. It threatens cereal production in most of Europe. The search for sources of resistance to this strain is proposed as the most efficient and safe solution to ensure high grain production. This will be helped by the development of high performance and low cost techniques for field phenotyping. In this study we analyzed vegetation indices in the Red, Green, Blue (RGB) images of crop canopies under field conditions. We evaluated their accuracy in predicting grain yield and assessing disease severity in comparison to other field measurements including the Normalized Difference Vegetation Index (NDVI), leaf chlorophyll content, stomatal conductance, and canopy temperature. We also discuss yield components and agronomic parameters in relation to grain yield and disease severity.RGB-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust (R 2 = 0.581 and R 2 = 0.536, respectively), far surpassing the predictive ability of NDVI (R 2 = 0.118 and R 2 = 0.128, respectively). In comparison to potential yield, we found the presence of disease to be correlated with reductions in the number of grains per spike, grains per square meter, kernel weight and harvest index. Grain yield losses in the presence of yellow rust were also greater in later heading varieties. The combination of RGB-based indices and days to heading together explained 70.9% of the variability in grain yield and 62.7% of the yield losses.(http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords:Wheat yellow rust Field phenotyping NDVI Phenology, Puccinia striiformis RGB-based indices Triticum durumT H E C R O P J O U R N A L 3 ( 2 0 1 5 ) 2 0 0 -2 1 0

UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

“…So far, the implementation of aerial platforms in HTPP programs has been extensive and successful in assessing crop performance under different management conditions. As a counterpart, even if potentially of lower throughput, ground-based phenotyping on single plots using cameras or sensors held by hand [15][16][17] or a pole [18] represent low-cost alternatives to aerial assessments. In addition, shorter distances between sensors and plant targets increase the data spatial resolution [19].The formulation of different wavelength indexes derived from multispectral and hyperspectral sensors and cameras is well established, and their applications to phenotyping range from measurements of biomass (e.g., normalized difference vegetation index, NDVI [20]) or water content (e.g., water band index, WBI [21]), to assessments of pigment composition (e.g., modified chlorophyll absorption ratio index, MCARI [22]).…”

“…So far, the implementation of aerial platforms in HTPP programs has been extensive and successful in assessing crop performance under different management conditions. As a counterpart, even if potentially of lower throughput, ground-based phenotyping on single plots using cameras or sensors held by hand [15][16][17] or a pole [18] represent low-cost alternatives to aerial assessments. In addition, shorter distances between sensors and plant targets increase the data spatial resolution [19].…”

Combined Use of Low-Cost Remote Sensing Techniques and δ13C to Assess Bread Wheat Grain Yield under Different Water and Nitrogen Conditions

“…The vegetation indices tested in this study, generated either spectroradiometrically (NDVI) or derived from RGB images, performed well when assessing differences in water conditions. The efficacy of these indices in capturing differences in growth and senescence in response to water regime has been reported for wheat already [32,33,54]. The choice of Figure 4.…”

“…The vegetation indices tested in this study, generated either spectroradiometrically (NDVI) or derived from RGB images, performed well when assessing differences in water conditions. The efficacy of these indices in capturing differences in growth and senescence in response to water regime has been reported for wheat already [32,33,54]. The choice of anthesis as the phenological stage for the remote sensing measurements was decided based on the results of Yousfi et al (2016) [54] under similar agro-ecological conditions as in the present study, together with an additional study under Mediterranean conditions where NDVI and RGB indices were measured periodically during the crop cycle [22], with robust correlations between those indices measured at anthesis and grain yield being reported.…”

“…The leaf chlorophyll content measured with a portable chlorophyll meter, which uses the same principle as the NDVI, but on the basis of the light transmitted through the leaf, has also been used extensively [30]. As an alternative, information derived from conventional digital Red-Green-Blue (RGB) images to formulate canopy vegetation indices is a low-cost and an easy proximal sensing approach to assess grain yield in cereals [31][32][33], even when limitations related to shadows and changes in ambient light conditions need to be taken into consideration [34]. Information derived from RGB images allows estimation of a wide range of crop traits in durum and bread wheat, such as early vigor, leaf area index, leaf senescence, aerial biomass and grain yield [31,33].…”