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Biblioteca(s): |
Embrapa Hortaliças. |
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Data corrente: |
13/03/2025 |
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Data da última atualização: |
13/03/2025 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ALVES, R. G.; LIMA, F.; GUEDES, I. M. R.; GIMENEZ, S. P. |
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Afiliação: |
RAFAEL GOMES ALVES, ENTRO UNIVERSITÁRIO FEI; FABIO LIMA, CENTRO UNIVERSITÁRIO FEI; ITALO MORAES ROCHA GUEDES, CNPH; SALVADOR PINILLOS GIMENEZ, CENTRO UNIVERSITARIO FEI. |
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Título: |
Dynamic light optimization in vertical farming using an IoT-driven digital twin framework and artificial intelligence. |
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Ano de publicação: |
2025 |
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Fonte/Imprenta: |
Applied Soft Computing, v. 174, Apr. 2025. |
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ISSN: |
1568-4946 |
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DOI: |
10.1016/j.asoc.2025.112985 |
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Idioma: |
Inglês |
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Notas: |
112985. |
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Conteúdo: |
The global agricultural sector faces mounting challenges from climate change, population growth, urbanization, and environmental degradation, necessitating innovative solutions to ensure food security. Urban and periurban agriculture, particularly vertical farming, offers a sustainable approach to increase food production while minimizing land use, reducing environmental impact, and enhancing resource efficiency. Unlike conventional, vertical farming systems that rely on static spectral recipes with fixed light compositions (e.g., Red-to-Blue ratios derived from historical data), this study introduces an Internet of Things-enabled smart vertical farming system that leverages digital twin technology and a genetic algorithm (GA) to dynamically optimize lettuce growth by adjusting RGB LED spectra throughout the crop cycle. The system monitors and controls key environmental parameters within a growth tower, including temperature, humidity, and lighting. A digital twin facilitates real-time data exchange between physical and virtual components, while the GA iteratively refines the light composition. Over a 34-day cultivation period, the algorithm identified an optimal RGB configuration (R:211, G:169, B:243; maximum intensity: 255) that aligns with spectral values reported in literature for lettuce, despite not directly measuring photobiological metrics such as Photosynthetic Photon Flux Density. To our knowledge, this is the first study to implement a dynamic, GA-driven spectral optimization strategy in vertical farming. While the objective was not to surpass traditional static lighting recipes, the results validate that adaptive methods can reliably converge to established optima. The IoT platform demonstrated robust capabilities in data collection, processing, and actuation, underscoring the promise of adaptive lighting strategies for controlled agriculture. Future research will focus on incorporating additional spectra (e.g., deep red, ultraviolet), automating data collection via image recognition, and analyzing energy efficiency to enhance scalability. MenosThe global agricultural sector faces mounting challenges from climate change, population growth, urbanization, and environmental degradation, necessitating innovative solutions to ensure food security. Urban and periurban agriculture, particularly vertical farming, offers a sustainable approach to increase food production while minimizing land use, reducing environmental impact, and enhancing resource efficiency. Unlike conventional, vertical farming systems that rely on static spectral recipes with fixed light compositions (e.g., Red-to-Blue ratios derived from historical data), this study introduces an Internet of Things-enabled smart vertical farming system that leverages digital twin technology and a genetic algorithm (GA) to dynamically optimize lettuce growth by adjusting RGB LED spectra throughout the crop cycle. The system monitors and controls key environmental parameters within a growth tower, including temperature, humidity, and lighting. A digital twin facilitates real-time data exchange between physical and virtual components, while the GA iteratively refines the light composition. Over a 34-day cultivation period, the algorithm identified an optimal RGB configuration (R:211, G:169, B:243; maximum intensity: 255) that aligns with spectral values reported in literature for lettuce, despite not directly measuring photobiological metrics such as Photosynthetic Photon Flux Density. To our knowledge, this is the first study to implement a dynamic, GA-driven spectral ... Mostrar Tudo |
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Palavras-Chave: |
Fazenda vertical. |
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Thesagro: |
Hortaliça. |
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Categoria do assunto: |
A Sistemas de Cultivo |
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Marc: |
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001 2173904
005 2025-03-13
008 2025 bl uuuu u00u1 u #d
022 $a1568-4946
024 7 $a10.1016/j.asoc.2025.112985$2DOI
100 1 $aALVES, R. G.
245 $aDynamic light optimization in vertical farming using an IoT-driven digital twin framework and artificial intelligence.$h[electronic resource]
260 $c2025
500 $a112985.
520 $aThe global agricultural sector faces mounting challenges from climate change, population growth, urbanization, and environmental degradation, necessitating innovative solutions to ensure food security. Urban and periurban agriculture, particularly vertical farming, offers a sustainable approach to increase food production while minimizing land use, reducing environmental impact, and enhancing resource efficiency. Unlike conventional, vertical farming systems that rely on static spectral recipes with fixed light compositions (e.g., Red-to-Blue ratios derived from historical data), this study introduces an Internet of Things-enabled smart vertical farming system that leverages digital twin technology and a genetic algorithm (GA) to dynamically optimize lettuce growth by adjusting RGB LED spectra throughout the crop cycle. The system monitors and controls key environmental parameters within a growth tower, including temperature, humidity, and lighting. A digital twin facilitates real-time data exchange between physical and virtual components, while the GA iteratively refines the light composition. Over a 34-day cultivation period, the algorithm identified an optimal RGB configuration (R:211, G:169, B:243; maximum intensity: 255) that aligns with spectral values reported in literature for lettuce, despite not directly measuring photobiological metrics such as Photosynthetic Photon Flux Density. To our knowledge, this is the first study to implement a dynamic, GA-driven spectral optimization strategy in vertical farming. While the objective was not to surpass traditional static lighting recipes, the results validate that adaptive methods can reliably converge to established optima. The IoT platform demonstrated robust capabilities in data collection, processing, and actuation, underscoring the promise of adaptive lighting strategies for controlled agriculture. Future research will focus on incorporating additional spectra (e.g., deep red, ultraviolet), automating data collection via image recognition, and analyzing energy efficiency to enhance scalability.
650 $aHortaliça
653 $aFazenda vertical
700 1 $aLIMA, F.
700 1 $aGUEDES, I. M. R.
700 1 $aGIMENEZ, S. P.
773 $tApplied Soft Computing$gv. 174, Apr. 2025.
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