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 | Acesso ao texto completo restrito à biblioteca da Embrapa Hortaliças. Para informações adicionais entre em contato com biblioteca@embrapa.br. |
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Registro Completo |
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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: |
LEADER 02740naa a2200217 a 4500
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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Registro original: |
Embrapa Hortaliças (CNPH) |
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Biblioteca |
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Origem |
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Registro Completo
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Biblioteca(s): |
Embrapa Agroindústria Tropical; Embrapa Meio Ambiente. |
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Data corrente: |
20/12/2023 |
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Data da última atualização: |
20/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
C - 0 |
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Autoria: |
PALAZZO, A. B.; BIASOTO, A. C. T.; DIONISIO, A. P.; CADENA, R. S.; BOLINI, H. M. A. |
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Afiliação: |
ALESSANDRA BUGATTE PALAZZO, UNIVERSIDADE ESTADUAL DE CAMPINAS; ALINE TELLES BIASOTO MARQUES, CNPMA; ANA PAULA DIONISIO, CNPAT; RAFAEL SILVA CADENA, UNIVERSIDADE FEDERAL DO ESTADO DO RIO DE JANEIRO; HELENA MARIA ANDRÉ BOLINI, UNIVERSIDADE ESTADUAL DE CAMPINAS. |
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Título: |
Time-intensity profile and relationship with consumer acceptability of processed cheese spread. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Research, Society and Development, v. 12, n. 7, e17612742672, 2023. |
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ISSN: |
2525-340 |
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DOI: |
http://dx.doi.org/10.33448/rsd-v12i7.42672 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Creamy “requeijão” is the commercially most prominent processed cheese spread in Brazil. The time intensity analysis is important to provide how the flavor behavior is for the consumers during the food ingestion and is used in order to obtain the temporal profile of an attribute in a certain product. In the present study, three different commercial brands, leaders in the market, in their traditional and light versions, were subjected to sensory and instrumental evaluations. Time-intensity analyses and acceptance tests were carried out. After the statistical evaluation, applying ANOVA, the Tukey test (p. 0.05), Partial Least Square (PLS), it was concluded that the consumers preferred products with a more intense cheese flavor and higher adhesiveness, besides expressing a greater acceptance and purchase intention for the product in its traditional version. |
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Palavras-Chave: |
Acceptance; Cheese; Instrumental; PLS; Sensory. |
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Thesagro: |
Análise Organoléptica; Requeijão. |
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Thesaurus NAL: |
Consumer acceptance; Cream cheese; Food analysis. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://www.alice.cnptia.embrapa.br/alice/bitstream/doc/1160079/1/Biasoto-Time-intensity-profile-2023.pdf
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Marc: |
LEADER 01794naa a2200313 a 4500
001 2160079
005 2023-12-20
008 2023 bl uuuu u00u1 u #d
022 $a2525-340
024 7 $ahttp://dx.doi.org/10.33448/rsd-v12i7.42672$2DOI
100 1 $aPALAZZO, A. B.
245 $aTime-intensity profile and relationship with consumer acceptability of processed cheese spread.$h[electronic resource]
260 $c2023
520 $aAbstract: Creamy “requeijão” is the commercially most prominent processed cheese spread in Brazil. The time intensity analysis is important to provide how the flavor behavior is for the consumers during the food ingestion and is used in order to obtain the temporal profile of an attribute in a certain product. In the present study, three different commercial brands, leaders in the market, in their traditional and light versions, were subjected to sensory and instrumental evaluations. Time-intensity analyses and acceptance tests were carried out. After the statistical evaluation, applying ANOVA, the Tukey test (p. 0.05), Partial Least Square (PLS), it was concluded that the consumers preferred products with a more intense cheese flavor and higher adhesiveness, besides expressing a greater acceptance and purchase intention for the product in its traditional version.
650 $aConsumer acceptance
650 $aCream cheese
650 $aFood analysis
650 $aAnálise Organoléptica
650 $aRequeijão
653 $aAcceptance
653 $aCheese
653 $aInstrumental
653 $aPLS
653 $aSensory
700 1 $aBIASOTO, A. C. T.
700 1 $aDIONISIO, A. P.
700 1 $aCADENA, R. S.
700 1 $aBOLINI, H. M. A.
773 $tResearch, Society and Development$gv. 12, n. 7, e17612742672, 2023.
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