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Biblioteca(s): |
Embrapa Milho e Sorgo. |
Data corrente: |
23/04/2024 |
Data da última atualização: |
29/05/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
GOMES, A. L. B.; FERNANDES, A. M. R.; HORTA, B. C.; OLIVEIRA, M. F. de. |
Afiliação: |
ANA L. B. GOMES, UNIVERSIDADE DO VALE DO ITAJAÍ; ANITA M. R. FERNANDES, UNIVERSIDADE DO VALE DO ITAJAÍ; BRUNO A. C. HORTA, UNIVERSIDADE DO VALE DO ITAJAÍ; MAURILIO FERNANDES DE OLIVEIRA, CNPMS. |
Título: |
Machine learning algorithms applied to weed management in integrated crop-livestock systems: a systematic literature review. |
Ano de publicação: |
2024 |
Fonte/Imprenta: |
Advances in Weed Science, v. 42, e020240047, 2024. |
DOI: |
https://doi.org/10.51694/AdvWeedSci/2024;42:00004 |
Idioma: |
Inglês |
Conteúdo: |
In recent times, there has been an environmental pressure to reduce the amount of pesticides applied to crops and, consequently, the crop production costs. Therefore, investments have been made in technologies that could potentially reduce the usage of herbicides on weeds. Among such technologies, Machine Learning approaches are rising in number of applications and potential impact. Therefore, this article aims to identify the main machine learning algorithms used in integrated crop-livestock systems for weed management. Based on a systematic literature review, it was possible to determine where the selected studies were performed and which crop types were mostly used. The main research terms in this study were: "machine learning algorithms" + "weed management" + "integrated crop-livestock system". Although no results were found for the three terms altogether, the combinations involving "weed management" + "integrated crop-livestock system" and "machine learning algorithms" + "weed management" returned a significant number of studies which were subjected to a second layer of refinement by applying an eligibility criteria. The achieved results show that most of the studies were from the United States and from nations in Asia. Machine vision and deep learning were the most used machine learning models, representing 28% and 19% of all cases, respectively. These systems were applied to different practical solutions, the most prevalent being smart sprayers, which allow for a site-specific herbicide application. MenosIn recent times, there has been an environmental pressure to reduce the amount of pesticides applied to crops and, consequently, the crop production costs. Therefore, investments have been made in technologies that could potentially reduce the usage of herbicides on weeds. Among such technologies, Machine Learning approaches are rising in number of applications and potential impact. Therefore, this article aims to identify the main machine learning algorithms used in integrated crop-livestock systems for weed management. Based on a systematic literature review, it was possible to determine where the selected studies were performed and which crop types were mostly used. The main research terms in this study were: "machine learning algorithms" + "weed management" + "integrated crop-livestock system". Although no results were found for the three terms altogether, the combinations involving "weed management" + "integrated crop-livestock system" and "machine learning algorithms" + "weed management" returned a significant number of studies which were subjected to a second layer of refinement by applying an eligibility criteria. The achieved results show that most of the studies were from the United States and from nations in Asia. Machine vision and deep learning were the most used machine learning models, representing 28% and 19% of all cases, respectively. These systems were applied to different practical solutions, the most prevalent being smart sprayers, which allow for a site... Mostrar Tudo |
Palavras-Chave: |
Image processing; Inteligência artificial; Processamento de imagem; Weed prevention. |
Thesagro: |
Erva Daninha. |
Thesaurus Nal: |
Artificial intelligence; Weed control. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1163826/1/Machine-learning-algorithms-applied-to-weed.pdf
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Marc: |
LEADER 02369naa a2200253 a 4500 001 2163826 005 2024-05-29 008 2024 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.51694/AdvWeedSci/2024;42:00004$2DOI 100 1 $aGOMES, A. L. B. 245 $aMachine learning algorithms applied to weed management in integrated crop-livestock systems$ba systematic literature review.$h[electronic resource] 260 $c2024 520 $aIn recent times, there has been an environmental pressure to reduce the amount of pesticides applied to crops and, consequently, the crop production costs. Therefore, investments have been made in technologies that could potentially reduce the usage of herbicides on weeds. Among such technologies, Machine Learning approaches are rising in number of applications and potential impact. Therefore, this article aims to identify the main machine learning algorithms used in integrated crop-livestock systems for weed management. Based on a systematic literature review, it was possible to determine where the selected studies were performed and which crop types were mostly used. The main research terms in this study were: "machine learning algorithms" + "weed management" + "integrated crop-livestock system". Although no results were found for the three terms altogether, the combinations involving "weed management" + "integrated crop-livestock system" and "machine learning algorithms" + "weed management" returned a significant number of studies which were subjected to a second layer of refinement by applying an eligibility criteria. The achieved results show that most of the studies were from the United States and from nations in Asia. Machine vision and deep learning were the most used machine learning models, representing 28% and 19% of all cases, respectively. These systems were applied to different practical solutions, the most prevalent being smart sprayers, which allow for a site-specific herbicide application. 650 $aArtificial intelligence 650 $aWeed control 650 $aErva Daninha 653 $aImage processing 653 $aInteligência artificial 653 $aProcessamento de imagem 653 $aWeed prevention 700 1 $aFERNANDES, A. M. R. 700 1 $aHORTA, B. C. 700 1 $aOLIVEIRA, M. F. de 773 $tAdvances in Weed Science$gv. 42, e020240047, 2024.
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Embrapa Milho e Sorgo (CNPMS) |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
Data corrente: |
27/10/1994 |
Data da última atualização: |
24/11/2022 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
PORTES, T. de A.; KLUTHCOUSKI, J.; SILVEIRA FILHO, A. |
Afiliação: |
TOMAS DE AQUINO PORTES E CASTRO, CNPAF; JOAO KLUTHCOUSKI, CNPAF; AUSTRELINO SILVEIRA FILHO, CNPAF. |
Título: |
Crescimento de Brachiaria brizantha e arroz em cultivo consorciado e em cultivo isolado. |
Ano de publicação: |
1993 |
Fonte/Imprenta: |
Revista Brasileira de Fisiologia Vegetal, São Carlos, v. 5, n. 1, p. 94, jan./jun. 1993. ref. 209. Edição dos Resumos do IV Congresso Brasileiro de Fisiologia Vegetal, Fortaleza, jul. 1993. |
Idioma: |
Português |
Palavras-Chave: |
Consórcio; Recuperação. |
Thesagro: |
Arroz; Brachiaria Brizantha; Oryza Sativa; Pastagem. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/127406/1/cbfv0001.pdf
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Marc: |
LEADER 00712nam a2200193 a 4500 001 1197814 005 2022-11-24 008 1993 bl uuuu u00u1 u #d 100 1 $aPORTES, T. de A. 245 $aCrescimento de Brachiaria brizantha e arroz em cultivo consorciado e em cultivo isolado. 260 $aRevista Brasileira de Fisiologia Vegetal, São Carlos, v. 5, n. 1, p. 94, jan./jun. 1993. ref. 209. Edição dos Resumos do IV Congresso Brasileiro de Fisiologia Vegetal, Fortaleza, jul. 1993.$c1993 650 $aArroz 650 $aBrachiaria Brizantha 650 $aOryza Sativa 650 $aPastagem 653 $aConsórcio 653 $aRecuperação 700 1 $aKLUTHCOUSKI, J. 700 1 $aSILVEIRA FILHO, A.
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Embrapa Arroz e Feijão (CNPAF) |
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