High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms

Jin, Xiuliang; Zarco‐Tejada, Pablo J.; Schmidhalter, Urs; Reynolds, Matthew; Hawkesford, Malcolm J.; Varshney, Rajeev K.; Yang, Tao; Nie, Chengwei; Li, Zhenhai; Ming, Bo; Xiao, Yonggui; Xie, Yi; Li, Shaokun

doi:10.1109/mgrs.2020.2998816

Cited by 156 publications

(70 citation statements)

References 319 publications

(379 reference statements)

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“…Conventional measurements of crop biomass mainly involve massive manpower investment and destructive sampling in the field [3]. These ground-based methods are time-consuming and not suitable for large-scale crop monitoring, since most of agronomical parameters exhibit high spatial variability within field [4]. Therefore, it is imperative to develop advanced and effective technologies to timely and economically obtain crop biomass information during growing seasons over large areas.…”

Section: Introductionmentioning

confidence: 99%

Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Yang

Song

et al. 2021

Remote Sensing

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Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

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Section: Introductionmentioning

confidence: 99%

Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Yang

Song

et al. 2021

Remote Sensing

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“…plant biomass). As consequence, it hinder capturing the plant longitudinal nature dynamics which can provide better phenotypic resolution required for adequate data interpretation [40]. To facilitate effective, accurate and continuous data collection, we applied a non-destructive image-based phenotyping approaches.…”

Section: Discussionmentioning

confidence: 99%

Genetic improvement of wheat early vigor promote weed-competitiveness under Mediterranean climate

Aharon

Fadida-Myers

Nashef

et al. 2020

Preprint

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Chemical weed-control is the most effective practice for wheat, however, rapid evolution of herbicide-resistant weeds threat food-security and calls for integration of non-chemical practices. We hypothesis that integration of GAR dwarfing-genes into elite wheat cultivars can promote early vigor and weed-competitiveness under Mediterranean climate. We develop near-isogenic lines of bread wheat cultivars with GAR dwarfing genes and evaluate them for early vigor and weed-competitiveness under various environmental and management conditions to identify promising NIL for weed-competitiveness and grain yield. While all three NILs, OC1 (Rht8,12), ZC4 (Rht12,13), and BNMF12 (Rht12), responded to gibberellic acid, they exhibited differences in early vigor. Greenhouse and field evaluation highlighted OC1 as a promising line, with significant advantage in early vigor over its parental. To facilitate accurate and continuous early vigor data collection, we applied non-destructive image-based phenotyping approaches which offers non-expensive and end-user friendly solution for selection. NIL OC1 was tested under different weed density level, infestation waves, and temperatures and highlight the complex genotypic × environmental × management interactions. Our findings demonstrate the potential of genetic modification of dwarfing genes as promising approach to improve weed-competitiveness, and serve as basis for future breeding efforts to support sustainable wheat production under semi-arid Mediterranean climate.

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“…This is due to the unique sensitivity of crop structure to microwaves, which effectively improves the availability of the satellite platform. WorldView series, RapidEye, GeoEye-1, SPOT series, QuickBird, Ikonos, Planet Scope, Pleiades series, KOMPSAT series, Satellite for Earth Observation series, Landsat series, Gaofen series, and SkySat series are currently the main satellite HT3Ps that can obtain color and multispectral images (Jin et al, 2020 ). Those satellite platforms able to obtain RADAR data are mainly Sentinel-1, RADARSAT-2, ENVISAT, TerraSAR-X/TanDEM-X, and RISAT-2 (Zhang et al, 2020 ).…”

Section: Ht3p For Field Phenotyping In Notoriously Heterogeneous Condmentioning

confidence: 99%

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

Quan

Song

et al. 2021

Front. Bioeng. Biotechnol.

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Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.

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High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms

Cited by 156 publications

References 319 publications

Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Genetic improvement of wheat early vigor promote weed-competitiveness under Mediterranean climate

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

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