Harnessing light heterogeneity to optimise controlled environment agriculture Article Swipe
Will Claydon
,
Ethan J. Redmond
,
Gina Y.W. Vong
,
Alana Kluczkovski
,
A. Thomas
,
Phoebe Sutton
,
Katherine Denby
,
Daphne Ezer
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.08.20.608762
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.08.20.608762
Yield is impacted by the environmental conditions that plants are exposed to. Controlled environmental agriculture provides growers with an opportunity to fine-tune environmental conditions for optimising yield and crop quality. However, space and time constraints will limit the number of experimental conditions that can be tested, which will in turn limit the resolution to which environmental conditions can be optimised. Here we present an innovative experimental approach that utilises the existing heterogeneity in light quantity and quality across a vertical farm to evaluate hundreds of environmental conditions concurrently. It proposes a three-phase workflow for identifying critical light variables, which can guide targeted improvements in yield and energy use. Using an observational study design, we identify features in light quality that are most predictive of biomass in different microgreens crops (kale, radish and sunflower) that may inform future iterations of lighting technology development for vertical farms. The findings suggest that light quality, rather than just light intensity, plays a crucial role in uniform crop yields and that light sensitivities are variety-specific, highlighting the importance of tailored light recipes for different crops.
Related Topics
Concepts
Agricultural engineering
Agriculture
Quality (philosophy)
Environmental science
Yield (engineering)
Workflow
Precision agriculture
Variety (cybernetics)
Limit (mathematics)
Biomass (ecology)
Computer science
Engineering
Mathematics
Agronomy
Ecology
Biology
Artificial intelligence
Mathematical analysis
Metallurgy
Philosophy
Database
Materials science
Epistemology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.08.20.608762
- https://www.biorxiv.org/content/biorxiv/early/2024/08/20/2024.08.20.608762.full.pdf
- OA Status
- green
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401854853
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401854853Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.08.20.608762Digital Object Identifier
- Title
-
Harnessing light heterogeneity to optimise controlled environment agricultureWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-08-20Full publication date if available
- Authors
-
Will Claydon, Ethan J. Redmond, Gina Y.W. Vong, Alana Kluczkovski, A. Thomas, Phoebe Sutton, Katherine Denby, Daphne EzerList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.08.20.608762Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2024/08/20/2024.08.20.608762.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2024/08/20/2024.08.20.608762.full.pdfDirect OA link when available
- Concepts
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Agricultural engineering, Agriculture, Quality (philosophy), Environmental science, Yield (engineering), Workflow, Precision agriculture, Variety (cybernetics), Limit (mathematics), Biomass (ecology), Computer science, Engineering, Mathematics, Agronomy, Ecology, Biology, Artificial intelligence, Mathematical analysis, Metallurgy, Philosophy, Database, Materials science, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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