UAV-multispectral and vegetation indices in soybean grain yield prediction based on in situ observation

Silva, Eder Eujácio da; Baio, Fábio Henrique Rojo; Teodoro, Larissa Pereira Ribeiro; Silva, Carlos Antônio da; Borges, Raisa Saraiva; Teodoro, Paulo Eduardo

doi:10.1016/j.rsase.2020.100318

Cited by 50 publications

(47 citation statements)

References 36 publications

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“…Zhao et al (2018) obtained high correlations using SAVI to diagnose N nutrition in plants for corn production. Silva et al (2020) reported successful prediction of soybean [Glycine max (L.) Merr.] grain yield by using SAVI.…”

Section: Canonical Correlationsmentioning

confidence: 99%

“…The overflight was performed at 100 m asl, allowing a spatial image resolution of 0.10 m. After obtaining the wavelengths, NDVI, NDRE, GNDVI, and SAVI were calculated. These procedures were performed according to Silva et al (2020). The agronomic traits evaluated were plant height (PH, m), first cob height (FCH, m), stem diameter (SD, cm), cob length (CL, cm), number of rows per cob (NRC), number of grains per row (NGR), and grain yield (YIE, kg ha −1 ).…”

Section: Core Ideasmentioning

confidence: 99%

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Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

et al. 2021

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The current challenge of corn (Zea mays L.) crops is to reach high yield to supply the world's demand for food, especially under different fertilization regimes and using the latest technology. We hypothesized that wavelengths and vegetation indices have a linear relationship with agronomic variables in corn. Our objectives were to verify the formation of super-traits and to study the association between wavelengths and vegetation indices with agronomic traits in corn cultivated under high and low topdressing N. The experiments were carried out in two crop seasons using a randomized block design with three replicates in a factorial scheme (11 genotypes × 2 contrasting levels of N: low, 60 kg ha -1 and high, 180 kg ha -1 ). At full bloom, the spectral variables green (550 nm), red (660 nm), red-edge (735 nm), and near-infrared (790 nm) and the vegetation indices normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation index (GNDVI), and soil-adjusted vegetation index (SAVI) were evaluated. Agronomic traits evaluated were leaf N, plant height, first pod height, stem diameter, cob length, rows per cob, grains per row, and grain yield. Results showed that SAVI, GNDVI, NDVI, and NDRE and red-edge and red were associated with agronomic traits in corn. The association between the agronomic traits evaluated here can be used to estimate the leaf N content and corn yield. INTRODUCTIONThe world demand for corn (Zea mays L.) consumption has increased, which has led to the need for increased production.To meet this growing demand, it is necessary to invest in crop

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Section: Canonical Correlationsmentioning

confidence: 99%

Section: Core Ideasmentioning

confidence: 99%

Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

et al. 2021

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“…This demonstrates that the VI’s evaluated here can be used as an auxiliary approach for evaluating the performance of soya bean plants submitted to different base saturation levels in the soil. VI’s have been used even during the crop development cycle aiming at obtaining additional information about its nutritional status, as well as its yield potential (Da Silva et al., 2020; Osco et al., 2020; Ramos et al., 2020).…”

Section: Resultsmentioning

confidence: 99%

High‐throughput phenotyping of soybean genotypes under base saturation stress conditions

Andrade

Teodoro

Baio

et al. 2021

J Agronomy Crop Science

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The search for high‐yielding genotypes and that are tolerant to abiotic stresses has been a major goal in plant breeding. Thus, the use of technologies such as precision agriculture associated with remote sensing tools for plant phenotyping has increased. The hypothesis of this research was that soya bean genotypes respond differently to low and adequate base saturation levels in the soil and that vegetation indexes can be efficient auxiliary tools in plant phenotyping for this purpose. The objective of this study was to evaluate the nutritional status and agronomic performance of soya bean genotypes grown in low and recommended base saturation conditions using high‐throughput phenotyping. The research was carried out in 2017/2018 and 2018/2019 crop seasons, in which two field experiments in each season were installed. In experiment I, genotypes (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10) were evaluated without soil correction (low saturation condition). In experiment II, liming was performed three months before sowing of the genotypes to raise the base saturation to 60% (recommended saturation). Canopy spectral behaviour at the following wavelengths was evaluated: green (550 nm), red (660 nm), red edge (735 nm) and near‐infrared (790 nm), and the vegetation indices (Vis) NDVI, SAVI, EVI and MSAVI were calculated. The variables evaluated were leaf calcium and magnesium contents and grain yield. The use of VI’s was efficient in assessing the performance of genotypes soya bean at different base saturation levels. The EVI showed moderate correlation with the nutritional and agronomic variables measured in each base saturation level. The approach used enabled both to identify genotypes tolerant to low base saturation soils and the ones with better response to liming.

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“…The broad diversity of remote sensors enables capturing different aspects of the plant phenotype. Different combinations of RGB, multispectral, and thermal image data associated with weather and soil have been employed to train deep learning models for crop yield forecasting ( Vega et al, 2015 ; Gracia-Romero et al, 2019 ; Zhang et al, 2019a ; Maimaitijiang et al, 2020 ; da Silva et al, 2020 ). The models can support differentiating crop performance in relation to irrigation regimes ( Gracia-Romero et al, 2019 ), quantify growth rate under nitrogen treatment ( Holman et al, 2016 ; Arroyo et al, 2017 ; Aranguren et al, 2020 ), estimate variation of wheat grain protein content ( Rodrigues et al, 2018 ; Sharabiani et al, 2019 ), and monitor crop height variation during the season ( Ziliani et al, 2018 ).…”

Section: Applications Of Htpmentioning

confidence: 99%

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

et al. 2021

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High throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors such as RGB cameras, hyperspectral sensors and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterise crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalising between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation and quantitative trait measurement. We emphasise the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

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UAV-multispectral and vegetation indices in soybean grain yield prediction based on in situ observation

Cited by 50 publications

References 36 publications

Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

High‐throughput phenotyping of soybean genotypes under base saturation stress conditions

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

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