Phenomics

Fritsche‐Neto, Roberto; Borém, Aluízio; Cobb, Joshua N.

doi:10.1002/9781118820971.ch7

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…Consumer level digital RGB cameras allow estimation of green biomass, senescence, and yield ( Sakamoto et al, 2012 ; Casadesus and Villegas, 2014 ). Multispectral or hyperspectral images can be formulated using different spectral indices for pigment degradation, senescence evaluation, photosynthetic efficiency, nutrient status or water content ( Gutierrez et al, 2010 ; Fritsche-Neto et al, 2015 ). Thermal images allow measurement of plant water status for phenotyping in the context of water-stress ( Jones et al, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

Clustering Field-Based Maize Phenotyping of Plant-Height Growth and Canopy Spectral Dynamics Using a UAV Remote-Sensing Approach

Liang

Yang

et al. 2018

Front. Plant Sci.

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Phenotyping under field environmental conditions is often considered as a bottleneck in crop breeding. Unmanned aerial vehicle high throughput phenotypic platform (UAV-HTPP) mounted with multi-sensors offers an efficiency, non-invasive, flexible and low-cost solution in large-scale breeding programs compared to ground investigation, especially where measurements are time-sensitive. This study was conducted at the research station of the Xiao Tangshan National Precision Agriculture Research Center of China. Using the UAV-HTPP, RGB and multispectral images were acquired during four critical growth stages of maize. We present a method of extracting plant height (PH) at the plot scale using UAV-HTPP based on the spatial structure of the maize canopy. The core steps of this method are segmentation and spatial Kriging interpolation based on multiple neighboring maximum pixels from multiple plants in a plot. Then, the relationships between the PH extracted from imagery collected using UAV-HTPP and the ground truth were examined. We developed a semi-automated pipeline for extracting, analyzing and evaluating multiple phenotypic traits: canopy cover (CC), normalized vegetation index (NDVI), PH, average growth rate of plant height (AGRPH), and contribution rate of plant height (CRPH). For these traits, we identify genotypic differences and analyze and evaluate dynamics and development trends during different maize growth stages. Furthermore, we introduce a time series data clustering analysis method into breeding programs as a tool to obtain a novel representative trait: typical curve. We classified and named nine types of typical curves of these traits based on curve morphological features. We found that typical curves can detect differences in the genetic background of traits. For the best results, the recognition rate of an NDVI typical curve is 59%, far less than the 82.3% of the CRPH typical curve. Our study provides evidence that the PH trait is among the most heritable and the NDVI trait is among the most easily affected by the external environment in maize.

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Section: Introductionmentioning

confidence: 99%

Clustering Field-Based Maize Phenotyping of Plant-Height Growth and Canopy Spectral Dynamics Using a UAV Remote-Sensing Approach

Liang

Yang

et al. 2018

Front. Plant Sci.

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“…Although our investigation was conceived originally as an extension of geometric morphometrics that addresses some of the issues that arise routinely in the context of morphological research, the methods we have described also have clear implications for the emerging field of phenomics [ 36 , 74 – 78 ]. Gene expression studies are beginning to unravel the complex interactions between genetic variation, environmental variation, and phenotypic variation [ 79 , 80 ].…”

Section: Discussionmentioning

confidence: 99%

Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania

MacLeod

Horwitz

2020

BMC Biol

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Background: Studies of mammalian sexual dimorphism have traditionally involved the measurement of selected dimensions of particular skeletal elements and use of single data-analysis procedures. Consequently, such studies have been limited by a variety of both practical and conceptual constraints. To compare and contrast what might be gained from a more exploratory, multifactorial approach to the quantitative assessment of form-variation, images of a small sample of modern Israeli gray wolf (Canis lupus) crania were analyzed via elliptical Fourier analysis of cranial outlines, a Naïve Bayes machine-learning approach to the analysis of these same outline data, and a deep-learning analysis of whole images in which all aspects of these cranial morphologies were represented. The statistical significance and stability of each discriminant result were tested using bootstrap and jackknife procedures. Results: Our results reveal no evidence for statistically significant sexual size dimorphism, but significant sexmediated shape dimorphism. These are consistent with the findings of prior wolf sexual dimorphism studies and extend these studies by identifying new aspects of dimorphic variation. Additionally, our results suggest that shapebased sexual dimorphism in the C. lupus cranial complex may be more widespread morphologically than had been appreciated by previous researchers. Conclusion: Our results suggest that size and shape dimorphism can be detected in small samples and may be dissociated in mammalian morphologies. This result is particularly noteworthy in that it implies there may be a need to refine allometric hypothesis tests that seek to account for phenotypic sexual dimorphism. The methods we employed in this investigation are fully generalizable and can be applied to a wide range of biological materials and could facilitate the rapid evaluation of a diverse array of morphological/phenomic hypotheses.

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“…High‐throughput phenotyping (HTP) is a nondestructive and rapid platform to monitor and measure multiple traits related to anatomy, physiology, or biochemical properties (Pabuayon et al., 2019). HTP aims to measure traits of many breeding lines more accurately, precisely, and economically (Fritsche‐Neto et al., 2014; Sandhu et al., 2022). Secondary traits are those that are not economically important, but they are correlated with the economically important primary traits.…”

Section: Introductionmentioning

confidence: 99%

Implementing multi‐trait genomic selection to improve grain milling quality in oats (Avena sativa L.)

Dhakal,

Poland,

Adhikari

et al. 2024

The Plant Genome

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Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high‐value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat‐based food products. Test weight, thins, and groat percentage are primary traits that define oat milling quality and the final price of food‐grade oats. Conventional selection for milling quality is costly and burdensome. Multi‐trait genomic selection (MTGS) combines information from genome‐wide markers and secondary traits genetically correlated with primary traits to predict breeding values of primary traits on candidate breeding lines. MTGS can improve prediction accuracy and significantly accelerate the rate of genetic gain. In this study, we evaluated different MTGS models that used morphometric grain traits to improve prediction accuracy for primary grain quality traits within the constraints of a breeding program. We evaluated 558 breeding lines from the University of Illinois Oat Breeding Program across 2 years for primary milling traits, test weight, thins, and groat percentage, and secondary grain morphometric traits derived from kernel and groat images. Kernel morphometric traits were genetically correlated with test weight and thins percentage but were uncorrelated with groat percentage. For test weight and thins percentage, the MTGS model that included the kernel morphometric traits in both training and candidate sets outperformed single‐trait models by 52% and 59%, respectively. In contrast, MTGS models for groat percentage were not significantly better than the single‐trait model. We found that incorporating kernel morphometric traits can improve the genomic selection for test weight and thins percentage.

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Phenomics

Cited by 4 publications

References 20 publications

Clustering Field-Based Maize Phenotyping of Plant-Height Growth and Canopy Spectral Dynamics Using a UAV Remote-Sensing Approach

Clustering Field-Based Maize Phenotyping of Plant-Height Growth and Canopy Spectral Dynamics Using a UAV Remote-Sensing Approach

Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania

Implementing multi‐trait genomic selection to improve grain milling quality in oats (Avena sativa L.)

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