Practical approach of spectroradiometry on the spectral signature identification of Asian soybean rust and construction of prediction model

• Grant number: 18/24869-0
• Support Opportunities: Regular Research Grants
• Start date: July 01, 2019
• End date: December 31, 2021
• Field of knowledge: Agronomical Sciences - Agronomy - Plant Health
• Principal Investigator: Carlos Gilberto Raetano
• Grantee: Carlos Gilberto Raetano
• Host Institution: Faculdade de Ciências Agronômicas (FCA). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil
• Associated researchers: Marcelo Giovanetti Canteri ; Matheus Mereb Negrisoli

Abstract

Monitoring is the basis of a successful disease management, providing input for decision-making on the timing and need of control. The Asian soybean rust (SBR) is the most important disease of the crop and is mainly managed with fungicides at "scheduled" periods. There is a large gap regarding the disease monitoring techniques, making monitoring through remote sensing of the most promising. The objective of this study is to obtain correlation between SBR progress on soybean crop with the reflectance values of the crop under disease infestation conditions, in order to propose a prediction model and use it as a monitoring method for decision-making of chemical control. The research projects will be conducted between 2019 and 2021 at the School of Agronomy (FCA/UNESP), Botucatu, SP, in three experiments. In Experiment (1), data will be collected in the field for correlation of SBR monitoring, comparing values obtained in disease severity monitoring, based on the number of pustules cm-2, reflectance values in bands to be established and reading of chlorophyll fluorescence and quantum yield of photosystem II. In Experiment (2), vegetative indexes (VI) will be calculated based on the readings and modelling of the correlation data for determination of IV or bands more responsive to the disease. Finally, in Experiment (3), it will be conducted the application of the monitoring by spectroradiometry in the P. pachyrhizi management in the field, determining different moments of control and assessing the pathogen control efficacy as well as the quali-quantitative characteristics of the spraying. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

NEGRISOLI, MATHEUS MEREB; NEGRISOLI, RAPHAELMEREB; DA SILVA, FLAVIONUNES; LOPES, LUCASDA SILVA; DE SOUZA JUNIOR, FRANCISCODE SALES; VELINI, EDIVALDO DOMINGUES; CARBONARI, CAIO ANTONIO; RODRIGUES, SERGIO AUGUSTO; RAETANO, CARLOS GILBERTO. Soybean rust detection and disease severity classification by remote sensing. AGRONOMY JOURNAL, v. 114, n. 6, p. 17-pg., 2022-09-15. (18/24869-0, 18/26486-0)

NEGRISOLI, MATHEUS MEREB; DA SILVA, FLAVIO NUNES; NEGRISOLI, RAPHAEL MEREB; LOPES, LUCAS DA SILVA; SOUZA JUNIOR, FRANCISCO DE SALES; DE FREITAS, BIANCA REZENDE; VELINI, EDIVALDO DOMINGUES; RAETANO, CARLOS GILBERTO. Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Control. AGRONOMY-BASEL, v. 12, n. 9, p. 23-pg., 2022-09-01. (18/26486-0, 18/24869-0)