The use of computer image analysis in a Lemna minor L. bioassay

Mazur, Robert; Szoszkiewicz, Krzysztof; Lewicki, Piotr P.; Bedla, Dawid

doi:10.1007/s10750-016-2972-7

Cited by 14 publications

(10 citation statements)

References 26 publications

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“…Results show that the newly proposed method is faster and as reliable as the other methods previously used to measure the growth rate of Lemnaceae [31][32]. Our experiment did not compare results with a fresh or dry weight of plants nevertheless, this was not the main aim of our work, and we considered reliable the correlation between weight and frond area [32].…”

Section: Discussionmentioning

confidence: 91%

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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Section: Discussionmentioning

confidence: 91%

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

Romano¹,

Iovane²,

Izzo³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Our method is faster and as reliable as the other methods previously used to measure the growth rate of Lemnaceae [ 34 , 35 ]; however, our experiment did not compare results with a fresh or dry weight of plants because this was not the main aim of our work, and we considered reliable the correlation between weight and frond area [ 35 ]. The main advantage of applying our method is that it drastically reduces the time required by the operator to analyse the growth rate in Lemnaceae to only the time needed to train the machine.…”

Section: Discussionmentioning

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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“…To facilitate rapid and accurate measurement of duckweed growth in toxicity tests, there has been a recent shift from frond counts and fresh or dry weight measurements to image analysis software, which can measure total area, plant health, and possibly flowering. The company Lemnatec sells commercial imaging chambers, with software packages, while researchers have applied the Aphelion software package [ 25 ], the NI vision Assistant software [ 26 ] in data analysis pipelines, or the free software Image J, with manual assistance in selecting green surface area (P. Fourounjian, unpublished) to take non-destructive measurements of biomass.…”

Section: Introductionmentioning

confidence: 99%

Flowering and Seed Production across the Lemnaceae

Fourounjian

Slovin

Messing

2021

IJMS

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Plants in the family Lemnaceae are aquatic monocots and the smallest, simplest, and fastest growing angiosperms. Their small size, the smallest family member is 0.5 mm and the largest is 2.0 cm, as well as their diverse morphologies make these plants ideal for laboratory studies. Their rapid growth rate is partially due to the family’s neotenous lifestyle, where instead of maturing and producing flowers, the plants remain in a juvenile state and continuously bud asexually. Maturation and flowering in the wild are rare in most family members. To promote further research on these unique plants, we have optimized laboratory flowering protocols for 3 of the 5 genera: Spirodela; Lemna; and Wolffia in the Lemnaceae. Duckweeds were widely used in the past for research on flowering, hormone and amino acid biosynthesis, the photosynthetic apparatus, and phytoremediation due to their aqueous lifestyle and ease of aseptic culture. There is a recent renaissance in interest in growing these plants as non-lignified biomass sources for fuel production, and as a resource-efficient complete protein source. The genome sequences of several Lemnaceae family members have become available, providing a foundation for genetic improvement of these plants as crops. The protocols for maximizing flowering described herein are based on screens testing daylength, a variety of media, supplementation with salicylic acid or ethylenediamine-N,N′-bis(2-hydroxyphenylacetic acid) (EDDHA), as well as various culture vessels for effects on flowering of verified Lemnaceae strains available from the Rutgers Duckweed Stock Cooperative.

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The use of computer image analysis in a Lemna minor L. bioassay

Cited by 14 publications

References 26 publications

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

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

Flowering and Seed Production across the Lemnaceae

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