Geoestatística e sistemas ‘fuzzy’ na proteção de plantas
Data
2006-11-16
Autores
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Editor
Universidade Federal de Lavras
Resumo
A Ferrugem Asiática (Phakopsora pachyrhizi H. Sydow & P. Sydow), relatada em diversas regiões do globo terrestre de climas tropicais e subtropicais, causa redução significativa na produtividade da soja (Glycine max L. Merr.). Fatores bióticos, como interação patógeno/hospedeiro, e abióticos influenciam o progresso da doença. Assim, objetivou-se neste trabalho estudar os efeitos do binômio temperatura e duração de molhamento foliar no processo monocíclico da ferrugem asiática nas cultivares Conquista, Savana e Suprema com base em um Sistema de Lógica ‘Fuzzy’ (SLF) e modelos de regressão não-linear desenvolvidos com base resultados experimentais. Para o desenvolvimento e validação do sistema, foi conduzido um experimento no Departamento de Fitopatologia da Universidade Federal de Lavras, em câmaras de crescimento vegetal nas temperaturas de 15 °C, 20 °C, 25 °C e 30 °C e períodos de molhamento foliar de 0, 6, 12, 18 e 24 horas. A inoculação foi realizada pulverizando-se as plantas com suspensão de 10 4 uredósporos de P. pachyrhizi.mL -1 de água. De posse dos dados, calculou-se a área abaixo da curva de progresso da doença (AACPD), utilizada como variável dependente para desenvolver e validar os modelos de regressão não-linear e SLF. Assim, pôde-se verificar melhor ou igual performance do SLF quando comparado aos modelos de regressão não-linear, para estimar a intensidade da ferrugem, à exceção da variável severidade para a cultivar Suprema. O sistema foi implementado com o uso de Sistema de Informações Geográficas e Geoestatística, sendo possível observar áreas favoráveis à doença, bem como a relação da intensidade da ferrugem com a evapotranspiração potencial e com o índice de umidade de Thornthwaite calculados em Minas Gerais.
The asian rust (Phakopsora pachyrhizi H. Sydow & P. Sydow) which has been reported in areas of tropical and subtropical climates around the world, causes significant soybean (Glycine max L. Merr.) productivity reduction. The disease progress is influenced by biotic factors, as pathogen/host interaction, and abiotic factors of the environment. Thus, the objective of this work was to study the effects of temperature and leaf wetness period in the asian rust monocyclic process on the Conquista, Savana and Suprema cultivars using Fuzzy Logic System (FLS) and non-linear regression models based on experimental data. For the system development and evaluation, an experiment was conducted at the Department of Plant Pathology at Federal University of Lavras, in growth chamber at temperatures of 15o, 20°, 25° and 30 °C and leaf wetness periods of 0, 6, 12, 18 and 24 hours. The plants were inoculated by spraying an inoculum suspension of P. pachyrhizi at concentration of 10 4 uredinospore.mL -1 . With the obtained disease progress data, the area under disease progress curves (AUDPC) was calculated and used as dependent variable to develop and validate the disease progress using non-linear regression and the FLS. As a result, it was verified the better or equal FLS performance when compared to non-linear regression models to estimate the soybean rust intensity, except to the Suprema cultivar severity variable. The system was implemented using Geographical Information Systems and Geostatistics. Thus, it was possible to map favorable areas to the disease progress and to observe the disease intensity relationship with the potential evapotranspiration and humidity Thoirnthwaite index calculated in Minas Gerais.
The asian rust (Phakopsora pachyrhizi H. Sydow & P. Sydow) which has been reported in areas of tropical and subtropical climates around the world, causes significant soybean (Glycine max L. Merr.) productivity reduction. The disease progress is influenced by biotic factors, as pathogen/host interaction, and abiotic factors of the environment. Thus, the objective of this work was to study the effects of temperature and leaf wetness period in the asian rust monocyclic process on the Conquista, Savana and Suprema cultivars using Fuzzy Logic System (FLS) and non-linear regression models based on experimental data. For the system development and evaluation, an experiment was conducted at the Department of Plant Pathology at Federal University of Lavras, in growth chamber at temperatures of 15o, 20°, 25° and 30 °C and leaf wetness periods of 0, 6, 12, 18 and 24 hours. The plants were inoculated by spraying an inoculum suspension of P. pachyrhizi at concentration of 10 4 uredinospore.mL -1 . With the obtained disease progress data, the area under disease progress curves (AUDPC) was calculated and used as dependent variable to develop and validate the disease progress using non-linear regression and the FLS. As a result, it was verified the better or equal FLS performance when compared to non-linear regression models to estimate the soybean rust intensity, except to the Suprema cultivar severity variable. The system was implemented using Geographical Information Systems and Geostatistics. Thus, it was possible to map favorable areas to the disease progress and to observe the disease intensity relationship with the potential evapotranspiration and humidity Thoirnthwaite index calculated in Minas Gerais.
Descrição
Tese de Doutorado defendida na Universidade Federal de Lavras
Palavras-chave
Geoprocessamento, Agricultura de precisão, Geociência, Geotecnologia, Geoinformática, Epidemiologia, Estatística, Ecologia, Clima, Grandes culturas, Semente, Inteligência artificial
Citação
ALVES, M. C. Geoestatística e sistemas ‘fuzzy’ na proteção de plantas. 2006. 186 f. Tese (Doutorado em Agronomia-Fitotecnia) - Universidade Federal de Lavras, Lavras. 2006.