Unmanned aerial system and satellite-based high resolution imagery for high-throughput phenotyping in dry bean

Sankaran, Sindhuja; Quirós, Juan José; Miklas, Phillip N.

doi:10.1016/j.compag.2019.104965

Cited by 47 publications

(46 citation statements)

References 42 publications

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“…In some high-yielding trials (e.g., 2018 Pullman trial), low correlation coefficients and prediction accuracy with image-based features were observed compared to other seasons and locations. Similar observations were found in dry bean studies ( Sankaran et al, 2018 , 2019 ). One possible explanation that Sankaran et al (2019) proposed may be that low canopy vigor resulted in great differences in the vegetation index values, which led to stronger correlations between ground truth and the vegetation index values.…”

Section: Discussionsupporting

confidence: 91%

“…In addition, new traits can be derived from high temporal resolution data, such as crop growth and development curves based on canopy area, vigor, and plant height ( Chang et al, 2017 ; Malambo et al, 2018 ), allowing plant breeders to assess development of each cultivar quantitatively and intensively. Current and previous studies demonstrated that seed yield or biomass of pulse or other crops can be predicted with image-based features ( Fieuzal et al, 2017 ; Yue et al, 2017 ; Anderson et al, 2019 ; Li et al, 2019 ; Sankaran et al, 2019 ; Moghimi et al, 2020 ). Different machine learning models, such as LASSO, SVM, and deep neural networks, have been tested for yield prediction.…”

Section: Discussionmentioning

confidence: 99%

“…In general, the performance of machine learning models improves with increased data quantity and quality, which may be exploited in future study. Additional features can also be considered when building such robust prediction models, such as modified chlorophyll absorption ratio index, photochemical reflectance index, normalized difference infrared index ( Harris Geospatial Solutions, 2020 ), plant height ( Bendig et al, 2015 ; Rueda-Ayala et al, 2019 ), and canopy temperature ( Sankaran et al, 2019 ; Zhang et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

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Crop Performance Evaluation of Chickpea and Dry Pea Breeding Lines Across Seasons and Locations Using Phenomics Data

et al. 2021

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The Pacific Northwest is an important pulse production region in the United States. Currently, pulse crop (chickpea, lentil, and dry pea) breeders rely on traditional phenotyping approaches to collect performance and agronomic data to support decision making. Traditional phenotyping poses constraints on data availability (e.g., number of locations and frequency of data acquisition) and throughput. In this study, phenomics technologies were applied to evaluate the performance and agronomic traits in two pulse (chickpea and dry pea) breeding programs using data acquired over multiple seasons and locations. An unmanned aerial vehicle-based multispectral imaging system was employed to acquire image data of chickpea and dry pea advanced yield trials from three locations during 2017–2019. The images were analyzed semi-automatically with custom image processing algorithm and features were extracted, such as canopy area and summary statistics associated with vegetation indices. The study demonstrated significant correlations (P < 0.05) between image-based features (e.g., canopy area and sum normalized difference vegetation index) with yield (r up to 0.93 and 0.85 for chickpea and dry pea, respectively), days to 50% flowering (r up to 0.76 and 0.85, respectively), and days to physiological maturity (r up to 0.58 and 0.84, respectively). Using image-based features as predictors, seed yield was estimated using least absolute shrinkage and selection operator regression models, during which, coefficients of determination as high as 0.91 and 0.80 during model testing for chickpea and dry pea, respectively, were achieved. The study demonstrated the feasibility to monitor agronomic traits and predict seed yield in chickpea and dry pea breeding trials across multiple locations and seasons using phenomics tools. Phenomics technologies can assist plant breeders to evaluate the performance of breeding materials more efficiently and accelerate breeding programs.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Crop Performance Evaluation of Chickpea and Dry Pea Breeding Lines Across Seasons and Locations Using Phenomics Data

et al. 2021

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“…Our results validate the utility of UAV in identifying QTLs in common bean. These are the first QTLs identified in this species using UAV-based imagery, although UAVs have been used for other purposes in the species (Trapp et al 2016, Sankaran et al 2018, Sankaran et al 2019. Use of UAVs to identify genetic variation will likely grow in the future as a complement to existing phenotyping methods.…”

Section: Discussionmentioning

confidence: 99%

Determining the Genetic Control of Common Bean Early-Growth Rate using Unmanned Aerial Vehicles

Parker¹,

Palkovic²,

Gepts³

2020

Preprint

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Vigorous early-season growth rate allows crops to compete more effectively against weeds and to conserve soil moisture in arid areas. Many crop species, including common bean, show genetic variation in growth rate between varieties. Despite this, the genetic basis of the early-season growth-related traits has not been well resolved in the species, in part due to historic phenotyping challenges. Using a range of UAV- and ground-based methods, we evaluated early-season growth vigor of two populations. These growth data were then used to find QTLs associated with several growth parameters. Our results suggest that early-season growth rate is the result of complex interactions between several genetic and environmental factors and highlight the need for the high-precision phenotyping provided by UAVs. The QTLs identified in this study are the first in common bean to be identified remotely using UAV technology and will be useful for developing crop varieties that compete with weeds more effectively. Ultimately, this will reduce crop losses and mitigate the need for herbicides and manual labor for weed control.

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“…A multi-rotor UAP, with an RGB camera and a fixed-wing UAP with a hyperspectral push-broom scanner, was devised by Habib et al (2017) to verify the feasibility of using RGB-based orthophotos to improve the geometric features of hyperspectral orthophotos. In addition, the combination of a UAP and four satellites was implemented to compare the phenotypic capabilities of different resolutions in dry bean (Sankaran et al, 2019), whose results indicated that using sub-meter resolution satellites as HT3Ps holds promising application prospects for field crop phenotyping. While some combinations of different types HT3Ps are still based on the time-consuming and laborious traditional field measurements, these will gradually disappear with the stabilization and improvement of the advanced HT3Ps.…”

Section: Ht3ps' Combination For Comparative Validationmentioning

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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Unmanned aerial system and satellite-based high resolution imagery for high-throughput phenotyping in dry bean

Cited by 47 publications

References 42 publications

Crop Performance Evaluation of Chickpea and Dry Pea Breeding Lines Across Seasons and Locations Using Phenomics Data

Crop Performance Evaluation of Chickpea and Dry Pea Breeding Lines Across Seasons and Locations Using Phenomics Data

Determining the Genetic Control of Common Bean Early-Growth Rate using Unmanned Aerial Vehicles

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

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