Time-Series Multispectral Indices from Unmanned Aerial Vehicle Imagery Reveal Senescence Rate in Bread Wheat

Hassan, Muhammad Adeel; Yang, Mengjiao; Rasheed, Awais; Jin, Xiuliang; Xia, Xianchun; Xiao, Yonggui; He, Zhonghu

doi:10.3390/rs10060809

Cited by 113 publications

(87 citation statements)

References 51 publications

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“…In aerial-based multispectral sensing, significant correlations between spectral indices and the yield were generally higher than those obtained from ground-based sensing, especially at booting and anthesis (Tables 3 and 4), suggesting that increased precision may be obtained from UAV imagery. This is in agreement with recent reports [12,31,32]. The relatively higher precision of measurements by UAVs can be associated with several major factors: (i) Non-vegetation pixels can be better removed from imagery obtained by UAV.…”

Section: Heritability Of Spectral Indices In Different Row Variantssupporting

confidence: 92%

Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

Knapp

Schmidhalter

2020

Remote Sensing

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Enhancing plant breeding to ensure global food security requires new technologies. For wheat phenotyping, only limited seeds and resources are available in early selection cycles. This forces breeders to use small plots with single or multiple row plots in order to include the maximum number of genotypes/lines for their assessment. High-throughput phenotyping through remote sensing may meet the requirements for the phenotyping of thousands of genotypes grown in small plots in early selection cycles. Therefore, the aim of this study was to compare the performance of an unmanned aerial vehicle (UAV) for assessing the grain yield of wheat genotypes in different row numbers per plot in the early selection cycles with ground-based spectral sensing. A field experiment consisting of 32 wheat genotypes with four plot designs (1, 2, 3, and 12 rows per plot) was conducted. Near infrared (NIR)-based spectral indices showed significant correlations (p < 0.01) with the grain yield at flowering to grain filling, regardless of row numbers, indicating the potential of spectral indices as indirect selection traits for the wheat grain yield. Compared with terrestrial sensing, aerial-based sensing from UAV showed consistently higher levels of association with the grain yield, indicating that an increased precision may be obtained and is expected to increase the efficiency of high-throughput phenotyping in large-scale plant breeding programs. Our results suggest that high-throughput sensing from UAV may become a convenient and efficient tool for breeders to promote a more efficient selection of improved genotypes in early selection cycles. Such new information may support the calibration of genomic information by providing additional information on other complex traits, which can be ascertained by spectral sensing.

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Section: Heritability Of Spectral Indices In Different Row Variantssupporting

confidence: 92%

Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

Knapp

Schmidhalter

2020

Remote Sensing

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“…Second, given that no SI-derived feature was more predictive of GY than scoring-derived features, we conclude that potential precision gains in estimating the switch from stay-green to remobilization using hyperspectral high throughput phenotyping techniques rather than visual scorings may be limited. It should be noted, however, that most of the SI used in this study were not developed for use in wheat canopies during senescence, and only few of them have been tested for their applicability during this growth stage (Erdle et al, 2013;Barmeier and Schmidhalter, 2017;Hassan et al, 2018). Significant relationships seem to be maintained during later growth stages, but tend to be unstable across stages (Erdle et al, 2013).…”

Section: Digital Senescence Phenotyping May Benefit Crop Breeding Primentioning

confidence: 99%

Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm

Anderegg

Aasen

et al. 2020

Front. Plant Sci.

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The ability of a genotype to stay green affects the primary target traits grain yield (GY) and grain protein concentration (GPC) in wheat. High throughput methods to assess senescence dynamics in large field trials will allow for (i) indirect selection in early breeding generations, when yield cannot yet be accurately determined and (ii) mapping of the genomic regions controlling the trait. The aim of this study was to develop a robust method to assess senescence based on hyperspectral canopy reflectance. Measurements were taken in three years throughout the grain filling phase on >300 winter wheat varieties in the spectral range from 350 to 2500 nm using a spectroradiometer. We compared the potential of spectral indices (SI) and full-spectrum models to infer visually observed senescence dynamics from repeated reflectance measurements. Parameters describing the dynamics of senescence were used to predict GY and GPC and a feature selection algorithm was used to identify the most predictive features. The three-band plant senescence reflectance index (PSRI) approximated the visually observed senescence dynamics best, whereas full-spectrum models suffered from a strong year-specificity. Feature selection identified visual scorings as most predictive for GY, but also PSRI ranked among the most predictive features while adding additional spectral features had littleeffect.Visuallyscoreddelayedsenescencewaspositivelycorrelatedwith GYrangingfromr= 0.173 in 2018 to r = 0.365 in 2016. It appears that visual scoring remains the gold standard to quantify leaf senescence in moderately large trials. However, using appropriate phenotyping platforms, the proposed index-based parameterization of the canopy reflectance dynamics offers the critical advantage of upscaling to very large breeding trials.

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“…Some efforts to apply remote sensing techniques to aerial images taken by UAVs have also been conducted. Information obtained by analyzing aerial images taken by UAV was utilized for breeding [16,17], cultivation management [18,19] and yield estimation [20,21]. For example, Mukoyama et al attempted to estimate the chlorophyll gauge value of rice by applying the vegetation index using the green and near-infrared spectral bands to the hyperspectral image [19].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

et al. 2019

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Reflectance and vegetation indices obtained from aerial images are often used for monitoring crop fields. In recent years, unmanned aerial vehicles (UAVs) have become popular and aerial images have been collected under various solar radiation conditions. The value of observed reflectance and vegetation indices are considered to be affected by solar radiation conditions, which may lead to inaccurate estimations of crop growth. In this study, in order to evaluate the effect of solar radiation conditions on aerial images, canopy reflectance in paddy fields was simulated by a radiative transfer model, FLiES (Forest Light Environmental Simulator), for various solar radiation conditions and canopy structures. Several parameters including solar zenith angle, proportion of diffuse light for incident sunlight, plant height, coordinates of plants and leaf area density, were tested in FLiES. The simulation results showed that the solar zenith angle did not vary the canopy reflectance under the conditions of the proportion of diffuse light at 1.0, but the variation was greater at lower proportions of diffuse light. The difference in reflectance caused by solar radiation was 0.01 and 0.1 at the maximum for red and near-infrared bands, respectively. The simulation results also showed that the differences in reflectance affect vegetation indices (Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2)). The variation caused by solar radiation conditions was the least for NDVI and the greatest for SR. However, NDVI was saturated at the least leaf area index (LAI), whereas SR was only slightly saturated. EVI2 was intermediate between SR and NDVI, both in terms of variation and saturation. The simulated reflectance and vegetation indices were similar to those obtained from the aerial images collected in the farmers’ paddy fields. These results suggest that a large proportion of diffuse light (close to 1.0) or a middle range of solar zenith angle (45 to 65 degrees) may be desirable for UAV monitoring. However, to maintain flexibility of time and occasion for UAV monitoring, EVI2 should be used to evaluate crop growth, although calibration based on solar radiation conditions is recommended.

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Time-Series Multispectral Indices from Unmanned Aerial Vehicle Imagery Reveal Senescence Rate in Bread Wheat

Cited by 113 publications

References 51 publications

Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm

Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

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