Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize

Adak, Alper; Murray, Seth C.; Anderson, Steven L.; Popescu, Sorin C.; Malambo, Lonesome; Romay, M. Cinta; León, Natalia de

doi:10.1002/tpg2.20102

Cited by 22 publications

(17 citation statements)

References 92 publications

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“…Plant selection at an early stage is challenging due to the impact of weather variability and the time needed for the plant to show discernible traits; however, studies in maize and soybean crops have shown that early phenotypic traits, collected with HTP platforms, can be used as an indicator of yield under optimal conditions [47,48]. In maize, a previous study using the 2017 G2F dataset identified loci associated with controlling plant height at early development, which showed a significant impact on yield prediction [48]. In our study, the use of early phenotypic data and genotype showed varied results, depending on the machine learning model employed, with the multimodal model presenting a higher prediction accuracy.…”

Section: Discussionmentioning

confidence: 99%

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Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection

et al. 2021

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Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to focus on the highest-performing varieties. Here, we present a multimodal deep learning model for predicting the performance of maize (Zea mays) at an early developmental stage, offering the potential to accelerate crop breeding. We employed multispectral images and eight vegetation indices, collected by an uncrewed aerial vehicle approximately 60 days after sowing, over three consecutive growing cycles (2017, 2018 and 2019). The multimodal deep learning approach was used to integrate field management and genotype information with the multispectral data, providing context to the conditions that the plants experienced during the trial. Model performance was assessed using holdout data, in which the model accurately predicted the yield (RMSE 1.07 t/ha, a relative RMSE of 7.60% of 16 t/ha, and R2 score 0.73) and identified the majority of high-yielding varieties, outperforming previously published models for early yield prediction. The inclusion of vegetation indices was important for model performance, with a normalized difference vegetation index and green with normalized difference vegetation index contributing the most to model performance. The model provides a decision support tool, identifying promising lines early in the field trial.

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Section: Discussionmentioning

confidence: 99%

“…In P2F1, the yield was consistently lower than other treatments because the hybrids were sown later in the season and experienced heat stress. A study using the same dataset hypothesized that delayed planting in that region of Texas, US, may increase photosynthetic activity, leading to increased plant height and lower yield [48].…”

Section: Discussionmentioning

confidence: 99%

Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection

et al. 2021

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“…It is also important to note that higher temporal and image resolution were provided in this study by lower flight altitude (25 m) and higher number of flights (13 and 17 time points) to generate the high throughput phenomic data that have been disregarded so far by most of the current literatures. Higher temporal and image resolutions were proposed to be important in predicting complex traits with higher accuracies, followed by the number of wavelengths and sensors 57 , 58 . Moreover, temporal resolution in high throughput phenomic data is required to dissect the growth stages in greater detail, particularly when the determination of critical time points as selection criteria are a goal in plant breeding programs.…”

Section: Discussionmentioning

confidence: 99%

Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms

DeSalvio

Adak

Murray

et al. 2022

Sci Rep

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Current methods in measuring maize (Zea mays L.) southern rust (Puccinia polyspora Underw.) and subsequent crop senescence require expert observation and are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery of elite maize hybrids planted in the 2020 and 2021 growing seasons, with 13 UAS flights obtained from 2020 and 17 from 2021. In total, 36 vegetation indices (VIs) were extracted from mosaicked aerial images that served as temporal phenomic predictors for southern rust scored in the field and senescence as scored using UAS-acquired mosaic images. Temporal best linear unbiased predictors (TBLUPs) were calculated using a nested model that treated hybrid performance as nested within flights in terms of rust and senescence. All eight machine learning regressions tested (ridge, lasso, elastic net, random forest, support vector machine with radial and linear kernels, partial least squares, and k-nearest neighbors) outperformed a general linear model with both higher prediction accuracies (92–98%) and lower root mean squared error (RMSE) for rust and senescence scores (linear model RMSE ranged from 65.8 to 2396.5 across all traits, machine learning regressions RMSE ranged from 0.3 to 17.0). UAS-acquired VIs enabled the discovery of novel early quantitative phenotypic indicators of maize senescence and southern rust before being detectable by expert annotation and revealed positive correlations between grain filling time and yield (0.22 and 0.44 in 2020 and 2021), with practical implications for precision agricultural practices.

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“…Numerous GWAS have been performed after the sequencing data and the associated rice seed materials became available (for example, [ 37 , 38 ]). Additionally, with the ease of high throughput phenotyping, GWAS has become a more attractive tool to dissect the molecular genetic control of breeding-relevant traits, for example, in rice [ 39 ], sorghum [ 40 ], and corn [ 41 ].…”

Section: Integrating Genetic Mapping and Genomics For Candidate Gene ...mentioning

confidence: 99%

Functional Allele Validation by Gene Editing to Leverage the Wealth of Genetic Resources for Crop Improvement

Thomson

Biswas

Tsakirpaloglou

et al. 2022

IJMS

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Advances in molecular technologies over the past few decades, such as high-throughput DNA marker genotyping, have provided more powerful plant breeding approaches, including marker-assisted selection and genomic selection. At the same time, massive investments in plant genetics and genomics, led by whole genome sequencing, have led to greater knowledge of genes and genetic pathways across plant genomes. However, there remains a gap between approaches focused on forward genetics, which start with a phenotype to map a mutant locus or QTL with the goal of cloning the causal gene, and approaches using reverse genetics, which start with large-scale sequence data and work back to the gene function. The recent establishment of efficient CRISPR-Cas-based gene editing promises to bridge this gap and provide a rapid method to functionally validate genes and alleles identified through studies of natural variation. CRISPR-Cas techniques can be used to knock out single or multiple genes, precisely modify genes through base and prime editing, and replace alleles. Moreover, technologies such as protoplast isolation, in planta transformation, and the use of developmental regulatory genes promise to enable high-throughput gene editing to accelerate crop improvement.

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Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize

Cited by 22 publications

References 92 publications

Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection

Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection

Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms

Functional Allele Validation by Gene Editing to Leverage the Wealth of Genetic Resources for Crop Improvement

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