Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass estimation from Unmanned Aerial System-based RGB imagery

Maimaitijiang, Maitiniyazi; Sagan, Vasit; Maimaitiyiming, Matthew; Hartling, Sean; Peterson, Kyle T.; Maw, Michael J.W.; Shakoor, Nadia; Mockler, Todd C.; Fritschi, Felix B.

doi:10.1016/j.isprsjprs.2019.03.003

Cited by 144 publications

(125 citation statements)

References 81 publications

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“…Some studies used spectral information [2,11,18,24,26,43,58,[60][61][62] and some structural information [1,8,22,23,28,34,48,50,55,[63][64][65]. Others used both [3][4][5][6]9,12,16,20,21,25,30,33,49,57,[66][67][68], while a few studies used spectral and structural metrics plus another data type [13,27,69] (Table A1). Within these categories, a wide range of species, study areas and methods are examined, demonstrating the applicability of UAS data to AGB estimation in agricultural and non-agricultural environments.…”

Section: Input Datamentioning

confidence: 99%

“…Mean height [3,9,12,13,15,16,[19][20][21]23,25,28,30,34,[48][49][50]57,58,63,65,[67][68][69][74][75][76] Maximum height [1,3,4,13,28,30,34,48,57,63,65,69] Minimum height [3,28,34,48,57,63,65,69] Median height [12,21,27,48,63,65,…”

Section: Heightmentioning

confidence: 99%

“…Total biomass is often separated into belowground and aboveground biomass (AGB). Measurements of AGB are logistically easier to collect and are valuable in both agricultural [3][4][5] and non-agricultural settings [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…In agricultural systems, AGB is a key agro-ecological indicator [4,10] that can be used to monitor crop growth, light use efficiency, carbon stock and physiological condition [3,5,6,[11][12][13][14][15][16]; predict crop yield and ensure yield quality [2,3,5,[12][13][14][17][18][19][20][21][22][23]; inform precision agriculture practices [3,15,[21][22][23]; maximize efficiency of fertilization and watering [4,10,14,21]; detect growth differences among phenotypes or cultivars [10,23]; calculate nitrogen content and assess nutrient status of plants [24][25][26]; and optimize economic decision-making throughout the growing season [3,14,15,19,22,27].…”

Section: Introductionmentioning

confidence: 99%

“…Forests, grasslands, wetlands, mangroves, dryland ecosystems and other vegetated areas provide important services for humans, such as carbon sequestration, oxygen production and biofuel, 2 of 46 as well as habitat for plant and animal species [9,[28][29][30]. Many ecosystems are also at increasing risk from climate change and land-use conversion and it is valuable to be able to quantify AGB at appropriate spatial and temporal scales and monitor it over time to assess the impacts of these changes on the global carbon cycle and to understand the resulting effects on ecosystem resilience and health [6,7,31,32] AGB is most accurately measured by collecting and weighing samples of vegetation [3,19,33] but this method is time-consuming, labor-intensive and destructive [16,34,35]. Allometric equations that relate AGB to measurable biophysical parameters like diameter at breast height (DBH), plant height or canopy area provide a way to estimate AGB more efficiently but require genus-or species-specific equations that must be developed and calibrated with direct biomass information [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

Poley

McDermid

2020

Remote Sensing

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Interest in the use of unmanned aerial systems (UAS) to estimate the aboveground biomass (AGB) of vegetation in agricultural and non-agricultural settings is growing rapidly but there is no standardized methodology for planning, collecting and analyzing UAS data for this purpose. We synthesized 46 studies from the peer-reviewed literature to provide the first-ever review on the subject. Our analysis showed that spectral and structural data from UAS imagery can accurately estimate vegetation biomass in a variety of settings, especially when both data types are combined. Vegetation-height metrics are useful for trees, while metrics of variation in structure or volume are better for non-woody vegetation. Multispectral indices using NIR and red-edge wavelengths normally have strong relationships with AGB but RGB-based indices often outperform them in models. Including measures of image texture can improve model accuracy for vegetation with heterogeneous canopies. Vegetation growth structure and phenological stage strongly influence model accuracy and the selection of useful metrics and should be considered carefully. Additional factors related to the study environment, data collection and analytical approach also impact biomass estimation and need to be considered throughout the workflow. Our review shows that UASs provide a capable tool for fine-scale, spatially explicit estimations of vegetation AGB and are an ideal complement to existing ground- and satellite-based approaches. We recommend future studies aimed at emerging UAS technologies and at evaluating the effect of vegetation type and growth stages on AGB estimation.

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Section: Input Datamentioning

confidence: 99%

Section: Heightmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

Poley

McDermid

2020

Remote Sensing

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Association between unmanned aerial vehicle high‐throughput canopy phenotyping and soybean yield

Casagrande

Sant’Ana²,

Meda³

et al. 2022

Agronomy Journal

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Identifying agronomic traits correlated to grain yield can be very useful for soybean [Glycine max (L.) Merr.] breeding, especially if these traits can be measured through unmanned aerial vehicle high-throughput phenotyping rather than through manual measurements. The objective of the present study was to assess the association between canopy coverage and soybean grain yield through different statistical methodologies. A panel with 97 soybean genotypes was evaluated in two field experiments conducted in Paraná State, Brazil. Canopy coverage was determined by using an RGB camera coupled to a drone. Images taken during flights at phenological stages V3-V4, V5-V6, V7-V8, and V9-R1 were used to calculate canopy coverage based on the green pixel ratio in each experimental unit. There were significant genotype × environment interactions in all evaluated traits. Selective accuracy values (0.73-0.96) revealed indirect yield selection efficiency based on canopy coverage. High genetic correlation estimates (0.76) were observed between grain yield and canopy coverage at flowering in one of the assessed environments. These results were confirmed through genetic correlation coefficient decomposition in direct and indirect effects and of gain estimates presenting indirect selection. Thus, canopy coverage data remotely collected using drones to soybean indirect selection for grain yield can be a promising strategy to accelerate genetic gains in soybean breeding programs.

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Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

et al. 2023

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High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS‐collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset.

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Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass estimation from Unmanned Aerial System-based RGB imagery

Cited by 144 publications

References 81 publications

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

Association between unmanned aerial vehicle high‐throughput canopy phenotyping and soybean yield

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

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