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Registro Completo |
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Biblioteca(s): |
Embrapa Hortaliças. |
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Data corrente: |
27/08/2025 |
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Data da última atualização: |
27/08/2025 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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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, CENTRO UNIVERSITÁRIO FEI; FÁBIO LIMA, CENTRO UNIVERSITÁRIO FEI; ITALO MORAES ROCHA GUEDES, CNPH; SALVADOR PINILOS GIMENEZ, CENTRO UNIVERSITÁRIO FEI. |
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Título: |
Proposal for a genetic algorithm-based approach to optimize light spectrum in vertical farming. |
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Ano de publicação: |
2025 |
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Fonte/Imprenta: |
In: WORKSHOP ON SEMICONDUCTORS AND MICRO & NANO TECHNOLOGY, 29., 2025, São Bernardo do Campo. Proceedings [...] São Bernardo do Campo: Centro Universitário FEI, 2025. |
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Idioma: |
Inglês |
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Notas: |
SEMINATEC 2025. |
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Conteúdo: |
Agriculture is crucial for global food security and economic development but faces significant challenges due to resource constraints and a global population that keeps increasing. Vertical farming, with its potential to optimize food production in land-scarce and environmentally stressed regions, emerges as a viable solution. However, the high-energy demands for lighting in vertical farms significantly impact operational expenses. This study introduces a novel Genetic Algorithm (GA) designed to optimize artificial lighting in vertical farms to enhance Light Use Efficiency (LUE). The proposed GA seeks to identify the optimal spectral composition of Red, Green, and Blue (RGB) LEDs, aiming to maximize crop productivity by evaluating characteristics such as height, width, fresh weight, and leaf count. The algorithm operates through ten stages, including initialization of a population, actuation of RGB values, fitness evaluation, and iterative processes of selection, crossover, mutation, and validation. By comparing RGB treatments with a reference cold white light treatment, the algorithm refines lighting conditions to improve crop performance at different growth stages. Detailed methodologies for fitness evaluation, crossover, mutation, and validation are provided, highlighting the practical steps for implementing this approach in vertical farming environments. This research aims to contribute to more energyefficient and productive vertical farming practices, supporting the broader goal of sustainable agricultural development. MenosAgriculture is crucial for global food security and economic development but faces significant challenges due to resource constraints and a global population that keeps increasing. Vertical farming, with its potential to optimize food production in land-scarce and environmentally stressed regions, emerges as a viable solution. However, the high-energy demands for lighting in vertical farms significantly impact operational expenses. This study introduces a novel Genetic Algorithm (GA) designed to optimize artificial lighting in vertical farms to enhance Light Use Efficiency (LUE). The proposed GA seeks to identify the optimal spectral composition of Red, Green, and Blue (RGB) LEDs, aiming to maximize crop productivity by evaluating characteristics such as height, width, fresh weight, and leaf count. The algorithm operates through ten stages, including initialization of a population, actuation of RGB values, fitness evaluation, and iterative processes of selection, crossover, mutation, and validation. By comparing RGB treatments with a reference cold white light treatment, the algorithm refines lighting conditions to improve crop performance at different growth stages. Detailed methodologies for fitness evaluation, crossover, mutation, and validation are provided, highlighting the practical steps for implementing this approach in vertical farming environments. This research aims to contribute to more energyefficient and productive vertical farming practices, supporting the bro... Mostrar Tudo |
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Palavras-Chave: |
Algoritmo genético; Artificial lighting; Genetic algorithm; Light Use Efficiency; Vertical farms. |
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Thesagro: |
Energia Elétrica; Fazenda. |
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Thesaurus Nal: |
Energy efficiency; Farms; Genetics; Lighting. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02496nam a2200289 a 4500
001 2178306
005 2025-08-27
008 2025 bl uuuu u00u1 u #d
100 1 $aALVES, R. G.
245 $aProposal for a genetic algorithm-based approach to optimize light spectrum in vertical farming.$h[electronic resource]
260 $aIn: WORKSHOP ON SEMICONDUCTORS AND MICRO & NANO TECHNOLOGY, 29., 2025, São Bernardo do Campo. Proceedings [...] São Bernardo do Campo: Centro Universitário FEI$c2025
500 $aSEMINATEC 2025.
520 $aAgriculture is crucial for global food security and economic development but faces significant challenges due to resource constraints and a global population that keeps increasing. Vertical farming, with its potential to optimize food production in land-scarce and environmentally stressed regions, emerges as a viable solution. However, the high-energy demands for lighting in vertical farms significantly impact operational expenses. This study introduces a novel Genetic Algorithm (GA) designed to optimize artificial lighting in vertical farms to enhance Light Use Efficiency (LUE). The proposed GA seeks to identify the optimal spectral composition of Red, Green, and Blue (RGB) LEDs, aiming to maximize crop productivity by evaluating characteristics such as height, width, fresh weight, and leaf count. The algorithm operates through ten stages, including initialization of a population, actuation of RGB values, fitness evaluation, and iterative processes of selection, crossover, mutation, and validation. By comparing RGB treatments with a reference cold white light treatment, the algorithm refines lighting conditions to improve crop performance at different growth stages. Detailed methodologies for fitness evaluation, crossover, mutation, and validation are provided, highlighting the practical steps for implementing this approach in vertical farming environments. This research aims to contribute to more energyefficient and productive vertical farming practices, supporting the broader goal of sustainable agricultural development.
650 $aEnergy efficiency
650 $aFarms
650 $aGenetics
650 $aLighting
650 $aEnergia Elétrica
650 $aFazenda
653 $aAlgoritmo genético
653 $aArtificial lighting
653 $aGenetic algorithm
653 $aLight Use Efficiency
653 $aVertical farms
700 1 $aLIMA, F.
700 1 $aGUEDES, I. M. R.
700 1 $aGIMENEZ, S. P.
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Embrapa Hortaliças (CNPH) |
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| 1. |  | CATUCHI, T. A.; SOUZA, G. M.; SORATTO, R. P.; FOLONI, J. S. S.; GUIDORIZZI, F. V. C.; BARBOSA, A. de M.; GUIDORIZI, K. A.; KUWAHARA, F. A. Biomassa de cultivares de soja sob déficit hídrico e adubação potássica. In: REUNIÃO BRASILEIRA DE FERTILIDADE DO SOLO E NUTRIÇÃO DE PLANTAS, 30.; REUNIÃO BRASILEIRA SOBRE MICORRIZAS, 14.; SIMPÓSIO BRASILEIRO DE MICROBIOLOGIA DO SOLO, 12.; REUNIÃO BRASILEIRA DE BIOLOGIA DO SOLO, 9.; SIMPÓSIO SOBRE SELÊNIO NO BRASIL, 1., 2012, Maceió. A responsabilidade socioambiental da pesquisa agrícola: anais. Viçosa: SBCS, 2012. 4 p. Trab. 1370. 1 CD-ROM. Fertbio.| Tipo: Artigo em Anais de Congresso |
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