Comparative Aerial and Ground Based High Throughput Phenotyping for the Genetic Dissection of NDVI as a Proxy for Drought Adaptive Traits in Durum Wheat

Condorelli, Giuseppe Emanuele; Maccaferri, Marco; Newcomb, Maria; Andrade-Sánchez, Pedro; White, Jeffrey W.; French, Andrew N.; Sciara, Giuseppe Notarbartolo di; Ward, Roxanne; Tuberosa, Roberto

doi:10.3389/fpls.2018.00893

Cited by 120 publications

(136 citation statements)

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“…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]). Canopy temperature measurements are used for the detection of changes in stomatal conductance and transpiration rates, as a response to the water status of the plant [23,24].…”

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

et al. 2019

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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.2 of 25 principal goal for breeders. Durum wheat is, by extension, the main cereal cultivated on the southern and eastern shores of the Mediterranean Basin and one of the main cereals in southern Europe [4].Yield is a phenotypically complicated trait, not only because of its genetic complexity [5], but also due to the relative magnitude of gene-environment interactions [6,7], and it is one of the most integrative traits influenced by known and unknown factors. Thus, genotype evaluations in multi-environment trials are needed, at least in the advanced (generations) stages of selection. However, the major point at issue is that high-throughput plant phenotyping (HTPP) may still represent a bottleneck in breeding programs [7], owing to the need to increase the accuracy, precision, and throughput of the methodologies used, while reducing costs and minimizing labor [8,9]. Furthermore, HTPP approaches should allow multi-temporal trait-specific measurements to evaluate the yield components at different phenological moments.Nowadays, and almost by definition, HTPP implies the use of non-invasive remote sensing approaches of different nature [5,10], given the possibility of screening larger populations faster than conventional phenotyping procedures. Moreover, recent progress and advances in the technology of aeronautics and sensors have allowed the adoption of unmanned aerial vehicle (UAV) platforms, capable of precisely screening hundreds of plots in a short period of time [7,11]. Further benefits of the simultaneous c...

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

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“…To overcome some of these limitations, fitting ultrasonic sonar to ground-based phenotyping platforms may provide accurate measurements. Indeed, the advantage of a ground-based platform is the suitability capturing data at high resolution with minimal errors [32].Generally, the performance of vegetative indices from UAS can be limited by saturation at higher levels of crop canopy cover [19]. Combining vegetative indices from photogrammetric imagery and plant height information may improve herbage yield [5].…”

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Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

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Sensor-based phenotyping technologies may offer a non-destructive, high-throughput and efficient assessment of herbage yield (HY) to replace current inefficient phenotyping methods. This paper assesses the feasibility of combining normalised difference vegetative index (NDVI) from multispectral imaging and ultrasonic sonar estimates of plant height to estimate HY of single plants in a large perennial ryegrass breeding program. For sensor calibration, fresh HY (FHY) and dry HY (DHY) were acquired destructively, and plant height was measured at four dates each in 2017 and 2018 from a selected subset of 480 plants. Global multiple linear regression models based on K-fold and random split cross-validation methods were used to evaluate the relationship between observed vs. predicted HY. The coefficient of determination (R 2 ) = 0.67-0.68 and a root mean square error (RMSE) between 5.43-7.60 g was obtained for the validation of predicted vs. observed DHY. The mean absolute error (MAE) and mean percentage error (MPE) ranged between 3.59-5.44 g and 22-28%, respectively. For the FHY, R 2 values ranged from 0.63 to 0.70, with an RMSE between 23.50 and 33 g, MAE between 15.11 and 24.34 g and MPE between~22% and 31%. Combining NDVI and plant height is a robust method to enable high-throughput phenotyping of herbage yield in perennial ryegrass breeding programs. current methods on large numbers plants or plots is slow and expensive, making it difficult to include large numbers of individual plants or plots [5]. This makes it challenging to capture an accurate representation of the population and estimate the correct values for individual genotypes. Therefore, HY estimation requires rapid, non-destructive phenotyping methods that can also facilitate genomic tools (e.g., genomic selection, GS) to shorten the breeding time and accelerate genetic gain. However, the number of plants for GS is likely higher than the number of plants traditionally used by breeders to perform selection breeding (e.g., The DairyBio initiative has 48,000 individual plants for genomic sub-selection breeding) where phenotyping of individual plants require accurate evaluation [5]. In recent years, high-throughput phenotyping (HTP) technologies have brought new insights to evaluate phenotypic traits efficiently in large breeding programs [6][7][8][9].Previous studies have used sensor-based data sources from aerial and ground-based platforms to estimate biophysical characteristics of various vegetations, including herbage yield of forage crops [5,10,11]. The aerial-based phenotyping platforms are suitable for lightweight red-green-blue (RGB), multispectral, and hyperspectral imaging systems and have used vegetative indices to build models for herbage yield [10,12] and biomass [13-15] estimation of pasture and cereal crops respectively. Normalised difference vegetative index (NDVI) is a widely used vegetative index for estimates of biomass [16][17][18] with limitations at high-biomass and density of crop cover [19]. Small, low-cost unmanned aerial systems (U...

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“…Although intense sampling is required to determine AUDPC, the use of unmanned aerial vehicles (UAVs) can increase data capturing capacity. For example, UAV‐based platforms measuring NDVI improved the speed of data acquisition and with greater accuracy for studying drought adaptive traits of durum wheat cultivars compared to ground‐based Greenseeker sensors (Condorelli et al, ). Furthermore, Shi et al () found that NDVI captured by UAV was strongly predictive of leaf area index and canopy cover, and could subsequently rank cultivars accordingly.…”

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Tolerance of wheat cultivars to root‐lesion nematode (Pratylenchus thornei) assessed by normalised difference vegetation index is predictive of grain yield

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Macdonald

et al. 2019

Annals of Applied Biology

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Tolerant wheat cultivars yield well when sown in fields infested with the root-lesion nematodePratylenchus thornei, which is present in 67% of fields in the subtropical grain region of eastern Australia. Wheat breeding programmes require accurate phenotyping to select germplasm with superior tolerance to P. thornei. This study investigated normalised difference vegetation index (NDVI) as a phenotypic tool to predict the tolerance of wheat cultivars on low and high P. thornei population densities. Three, 2-year field experiments used a resistant and a susceptible wheat cultivar in the first year to develop low and high P. thornei populations. In the second year, 36 wheat cultivars were sown on these plots. A NTech Greenseeker was used to determine the NDVI of each plot at regular times during the season and grain yield was measured at crop maturity. There was an inverse relationship between P. thornei population densities and the NDVI for intolerant wheat cultivars. Regression analysis showed a highly predictive response between the yield tolerance index and NDVI with R 2 ranging from 0.85 (n = 36) to 0.93 (n = 36) for the three experiments. The area under the disease progress curve with respect to NDVI was highly predictive of yield tolerance (R 2 = 0.92; n = 36) when there were high populations (9,091 P. thornei/kg), but not when populations were low (578 P. thornei/kg). Tolerant cultivars can be identified by NDVI when sown on soil containing high populations (>2,500 P. thornei/kg) by measurement at approximately 1,000 degree days after sowing. Greenseeker is a valuable tool for wheat breeders to select germplasm with tolerance of P. thornei. K E Y W O R D SGreenseeker, normalised difference vegetation index, Pratylenchus thornei tolerance, wheat, yield loss

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Comparative Aerial and Ground Based High Throughput Phenotyping for the Genetic Dissection of NDVI as a Proxy for Drought Adaptive Traits in Durum Wheat

Cited by 120 publications

References 75 publications

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

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

Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

Tolerance of wheat cultivars to root‐lesion nematode (Pratylenchus thornei) assessed by normalised difference vegetation index is predictive of grain yield

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