Radiometric Calibration of ‘Commercial off the Shelf’ Cameras for UAV-Based High-Resolution Temporal Crop Phenotyping of Reflectance and NDVI

Holman, Fenner H.; Riche, A. B.; Castle, March; Wooster, Martin J.; Hawkesford, Malcolm J.

doi:10.3390/rs11141657

Cited by 29 publications

(19 citation statements)

References 34 publications

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“…Detailed datasets can reveal hitherto unrecognized information concerning the genetic control of performance revealed at different developmental stages ( Lyra et al., 2020 ). Similar datasets can be obtained from drone-based platforms which are able to cover larger trials at multiple sites, but require greater manual inputs for collection ( Holman et al., 2016 ; Holman et al., 2019 ).…”

Section: Prospects: Research Gaps and Using Automated Phenotyping Formentioning

confidence: 99%

Impacts of G x E x M on Nitrogen Use Efficiency in Wheat and Future Prospects

Hawkesford

Riche

2020

Front. Plant Sci.

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Globally it has been estimated that only one third of applied N is recovered in the harvested component of grain crops. This represents an incredible waste of resource and the overuse has detrimental environmental and economic consequences. There is substantial variation in nutrient use efficiency (NUE) from region to region, between crops and in different cropping systems. As a consequence, both local and crop specific solutions will be required for NUE improvement at local as well as at national and international levels. Strategies to improve NUE will involve improvements to germplasm and optimized agronomy adapted to climate and location. Essential to effective solutions will be an understanding of genetics (G), environment (E), and management (M) and their interactions (G x E x M). Implementing appropriate solutions will require agronomic management, attention to environmental factors and improved varieties, optimized for current and future climate scenarios. As NUE is a complex trait with many contributing processes, identifying the correct trait for improvement is not trivial. Key processes include nitrogen capture (uptake efficiency), utilization efficiency (closely related to yield), partitioning (harvest index: biochemical and organ-specific) and trade-offs between yield and quality aspects (grain nitrogen content), as well as interactions with capture and utilization of other nutrients. A long-term experiment, the Broadbalk experiment at Rothamsted, highlights many factors influencing yield and nitrogen utilization in wheat over the last 175 years, particularly management and yearly variation. A more recent series of trials conducted over the past 16 years has focused on separating the key physiological sub-traits of NUE, highlighting both genetic and seasonal variation. This perspective describes these two contrasting studies which indicate G x E x M interactions involved in nitrogen utilization and summarizes prospects for the future including the utilization of high throughput phenotyping technology.

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Section: Prospects: Research Gaps and Using Automated Phenotyping Formentioning

confidence: 99%

Impacts of G x E x M on Nitrogen Use Efficiency in Wheat and Future Prospects

Hawkesford

Riche

2020

Front. Plant Sci.

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“…Remote sensing via unmanned aerial vehicles (UAV) is currently being investigated as a means to close the gap because of its capability to acquire the high temporal and spatial resolution data required for high throughput phenotyping over relatively limited areas. UAVs can collect huge quantities of data "on demand", providing opportunities for estimation and prediction of a wide range of agronomic traits [12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

et al. 2020

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High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

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“…Currently, UAVs can carry different sensors (RGB cameras, multi/hyperspectral cameras, thermal cameras, and lidar) to estimate different crop traits at varying spatial scales. Images from UAVs can be employed to estimate grain yield [99], canopy temperature [266] and NDVI [259], [282], plant height [108], [282], [283], biomass [162], GAI [109], lodging [134], plant density [39], fluorescence [266], and nitrogen status [36], mostly through proxies for plant traits. The advantage of UAVs concerns the relatively high-resolution images that are obtainable in a relatively short time; however, it is difficult to cover very large areas due to the vehicles' limited range and speed.…”

Section: Aerial Phenotyping Platformsmentioning

confidence: 99%

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

Jin

Zarco‐Tejada

Schmidhalter

et al. 2021

IEEE Geosci. Remote Sens. Mag.

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180

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This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. 2 0274-6638/20©2020IEEE IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE MONTH 2020 C rop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields. BACKGROUND Worldwide demand for food will increase through 2050 and beyond due to the increasing global human population. This represents a huge challenge to crop researchers and agricultural policymakers because current yield gain rates will not be sufficient for the demands of population growth, while climate change will make the difficulty even greater. Today's DNA sequencing, marker-assisted breeding, transgenic technology, genome-wide association study (GWAS) approaches, and quantitative trait loci (QTL) identification have been applied, to a limited extent, to improve crop yields [1]-[4]. While it is now relatively easy to select for monogenic traits, current genome sequence datasets have not been sufficiently mined for more genetically complex (multigenic) performance characteristics, at least in part because of the lack of crop phenotypic information collected from real-world field situations. Furthermore, traditional crop growth analysis often involves destructive sampling that is time-consuming and prone to measurement error. At High-Throughput Estimation of Crop Traits A review of ground and aerial phenotyping platforms

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Radiometric Calibration of ‘Commercial off the Shelf’ Cameras for UAV-Based High-Resolution Temporal Crop Phenotyping of Reflectance and NDVI

Cited by 29 publications

References 34 publications

Impacts of G x E x M on Nitrogen Use Efficiency in Wheat and Future Prospects

Impacts of G x E x M on Nitrogen Use Efficiency in Wheat and Future Prospects

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

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

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