Digital insights: bridging the phenotype-to-genotype divide

McCabe, Matthew F.; Tester, Mark

doi:10.1093/jxb/erab108

Cited by 9 publications

(7 citation statements)

References 25 publications

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“…Ivushkin et al (2019) combined plant height from a LiDAR scanner, the UAV-based hyperspectral physiological reflectance index (PRI) and canopy temperature from UAV-based thermal cameras to increase the discrimination of quinoa plants in the control and salt-treated plots. While all of these studies showcase the potential of UAV data to detect salt stress in crop, the underlying genetic insights that deliver the observable phenotypic features need to be investigated (McCabe & Tester, 2021). Such investigations will be assisted by the collection of additional phenotypic traits and identification of the most suitable image datasets and modeling approaches from which to derive these observable features.…”

Section: Discussionmentioning

confidence: 99%

Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index, SPAD-based chlorophyll and a random forest approach

et al. 2022

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Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned aerial vehicles (UAVs) provide a promising means for phenotyping and offer the potential for new insights into relative plant performance. During a field trial exploring 141 quinoa accessions, a UAV-based multispectral camera was deployed to retrieve leaf area index (LAI) and SPAD-based chlorophyll across 378 control and 378 saline-irrigated plots using a random forest regression approach based on both individual spectral bands and 25 different vegetation indices (VIs) derived from the multispectral imagery. Results show that most VIs had stronger correlation with the LAI and SPAD-based chlorophyll measurements than individual bands. VIs including the red-edge band had high importance in SPAD-based chlorophyll predictions, while VIs including the near infrared band (but not the red-edge band) improved LAI prediction models. When applied to individual treatments (i.e. control or saline), the models trained using all data (i.e. both control and saline data) achieved high mapping accuracies for LAI (R2 = 0.977–0.980, RMSE = 0.119–0.167) and SPAD-based chlorophyll (R2 = 0.983–0.986, RMSE = 2.535–2.861). Overall, the study demonstrated that UAV-based remote sensing is not only useful for retrieving important phenotypic traits of quinoa, but that machine learning models trained on all available measurements can provide robust predictions for abiotic stress experiments.

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Section: Discussionmentioning

confidence: 99%

Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index, SPAD-based chlorophyll and a random forest approach

et al. 2022

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“…Furthermore, we compared utilising differences between the two measurement techniques with the Bland-Altman plot to underline the presence of bias between the two methods. As described by Dogan [29], Bland and Altman's limits of agreement (LoA) have conventionally been used in medical research to evaluate the agreement between two methods of measurement for quantitative variables [7]. Nevertheless, Bland and Altman's LoA method may be misleading in the presence of heteroskedastic distributions [8].…”

Section: Discussionmentioning

confidence: 99%

“…In several areas of study (including genetics), the requirement of an image analysis software that could quantify tiny differences among plants phenotypes has been mandatory and has led us to enter the so-called "big data era" in plant science [3][4]. Thanks to the breach made by the genetic field, this software analysis method soon became mandatory in numerous other areas, such as botany, agronomy, and forestry [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Method to Assess Growth Patterns in Plants of the Family Lemnaceae

Romano¹,

Iovane²,

Izzo³

et al. 2022

Preprint

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Numerous new technologies have been implemented in image analysis methods that help researchers withdraw scientific conclusions from biological phenomena. Plants of the family Lemnaceae (duckweeds) are the smallest flowering plants in the world, and biometric measurements of single plants and their growth rate are highly challenging. Although the use of software for digital image analysis has changed the way scientists extract phenomenological data (also for studies on duckweeds), the procedure is often not wholly automated and sometimes relies on the intervention of a human operator. Such a constraint can limit the objectivity of the measurements and generally slows down the time required to produce scientific data. Here is the need to implement image analysis software with artificial intelligence that can substitute the human operator. In this paper, we present a new method to study the growth rates of the plants of the Lemnaceae family based on the application of machine learning procedures to digital image analysis. The method is compared to existing analogical and computer-operated procedures. Results showed that our method drastically reduces the time consumption of the human operator while retaining a high correlation in the growth rates measured with other procedures As expected, machine learning methods applied to digital image analysis can overtake the constraints of measuring growth rates of very small plants and might help duckweeds gain worldwide attention thanks to their great nutritional qualities and biological plasticity.

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“…In several areas of study (including genetics), the requirement of image analysis software that could quantify tiny differences among plants’ phenotypes has been mandatory and has led us to enter the so-called “big data era” in plant science [ 3 , 4 ]. Thanks to the breach made by the genetic field, this software analysis method soon became mandatory in numerous other areas, such as botany, agronomy, and forestry [ 5 , 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning Method to Assess Growth Patterns in Plants of the Family Lemnaceae

Romano

Iovane

Izzo

et al. 2022

Plants

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Numerous new technologies have been implemented in image analysis methods that help researchers draw scientific conclusions from biological phenomena. Plants of the family Lemnaceae (duckweeds) are the smallest flowering plants in the world, and biometric measurements of single plants and their growth rate are highly challenging. Although the use of software for digital image analysis has changed the way scientists extract phenomenological data (also for studies on duckweeds), the procedure is often not wholly automated and sometimes relies on the intervention of a human operator. Such a constraint can limit the objectivity of the measurements and generally slows down the time required to produce scientific data. Herein lies the need to implement image analysis software with artificial intelligence that can substitute the human operator. In this paper, we present a new method to study the growth rates of the plants of the Lemnaceae family based on the application of machine-learning procedures to digital image analysis. The method is compared to existing analogical and computer-operated procedures. The results showed that our method drastically reduces the time consumption of the human operator while retaining a high correlation in the growth rates measured with other procedures. As expected, machine-learning methods applied to digital image analysis can overcome the constraints of measuring growth rates of very small plants and might help duckweeds gain worldwide attention thanks to their strong nutritional qualities and biological plasticity.

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Digital insights: bridging the phenotype-to-genotype divide

Abstract: This article comments on: Han R, Wong AJY, Tang Z, Truco MJ, Lavelle DO, Kozik A, Jin Y, Michelmore R. 2021. Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce. Journal of Experimental Botany 72,2979–2994.

Cited by 9 publications

References 25 publications

Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index, SPAD-based chlorophyll and a random forest approach

Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index, SPAD-based chlorophyll and a random forest approach

A Machine Learning Method to Assess Growth Patterns in Plants of the Family Lemnaceae

A Machine-Learning Method to Assess Growth Patterns in Plants of the Family Lemnaceae

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