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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... 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
Marc:  Mostrar Marc Completo
Registro original:  Embrapa Milho e Sorgo (CNPMS)
Biblioteca ID Origem Tipo/Formato Classificação Cutter Registro Volume Status URL
CNPMS30317 - 1UPCAP - DD
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Biblioteca(s):  Embrapa Pesca e Aquicultura; Embrapa Unidades Centrais.
Data corrente:  14/11/2016
Data da última atualização:  17/11/2016
Tipo da produção científica:  Artigo em Periódico Indexado
Circulação/Nível:  A - 2
Autoria:  BORTOLON, L.; ERNANI, P. R.; BORTOLON, E. S. O.; GIANELO, C.; ALMEIDA, R. G. O. de; WELTER, S.; ROGERI, D. A.
Afiliação:  LEANDRO BORTOLON, CNPASA; PAULO ROBERTO ERNANI, UESC; ELISANDRA SOLANGE OLIVEIRA BORTOLON, CNPASA; CLESIO GIANELLO, UFRGS; RODRIGO GABRIEL OLIVEIRA DE ALMEIDA, UFRGS; SAMUEL WELTER, UFRGS; DOUGLAS ANTONIO ROGERI, Faculdade de Itapiranga.
Título:  Degree of phosphorus saturation threshold for minimizing P losses by runoff in cropland soil of Southern Brazil.
Ano de publicação:  2016
Fonte/Imprenta:  Pesquisa Agropecuária Brasileira, Brasília, DF, v. 51, n. 9, p. 1088-1098, set. 2016.
Idioma:  Inglês
Conteúdo:  The objective of this work was to assess the risk of phosphorus losses by runoff through an index based on the degree of P saturation (DPS), in cropland soils of Southern Brazil. Sixty-five highly representative cropland soils from the region were evaluated. Three labile P forms were measured (Mehlich-1, Mehlich-3, and ammonium oxalate), and four P sorption indexes were tested (phosphorus single sorption point and Fe+Al determined with the three extractors). Water-extractable P (WEP) was used as an index of P susceptibility to losses by surface runoff. The DPS was determined from the ratio between labile P and each sorption index. DPS values obtained from the ratio between Mehlich-1 P and the single P sorption point ranged from 1 to 25%, whereas those from Mehlich-1 P and Fe+Al (ammonium oxalate) ranged from 1 to 55%. All DPS types were highly correlated with WEP. From a practical stand point, the DPS obtained with both P and Fe+Al extracted with Mehlich-1 can be used to estimate the risk of P losses by runoff in soils of Southern Brazil.
Palavras-Chave:  Escorrimento superficial; P em único ponto; Qualidade de água; Single P sorption point.
Thesagro:  Eutrofização.
Thesaurus NAL:  Eutrophication; Runoff; Water quality.
Categoria do assunto:  --
URL:  https://ainfo.cnptia.embrapa.br/digital/bitstream/item/150006/1/Degree-of-phosphorus-saturation.pdf
Marc:  Mostrar Marc Completo
Registro original:  Embrapa Pesca e Aquicultura (CNPASA)
Biblioteca ID Origem Tipo/Formato Classificação Cutter Registro Volume Status
AI-SEDE60166 - 1UPEAP - PP630.72081P474
CNPASA429 - 1UPCAP - DDCNPASA_AP342016.034
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