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Agronomic performance evaluation of bean cultivars co-inoculated with R. tropici and A. brasilense, treated with Phosphorus fertilization, evaluated through drone phenotyping combined with the artificial neural networks methodology

Grant number: 21/03130-9
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): August 01, 2021
Effective date (End): July 31, 2022
Field of knowledge:Agronomical Sciences - Agronomy - Crop Science
Principal researcher:Rafael Simões Tomaz
Grantee:Franco Monici Fabrino
Home Institution: Faculdade de Ciências Agrárias e Tecnológicas. Universidade Estadual Paulista (UNESP). Campus de Dracena. Dracena , SP, Brazil

Abstract

The common bean (Phaseolus vulgaris L.) is one of the most used foods in Brazilian cuisine, much because of its high protein content contained in its grains, besides the presence of essential nutrients in its composition. The adoption of technologies, such as the use of improved cultivars, inoculation and co-inoculation, rational use of phosphate fertilizer, combined with the most current analysis technologies such as high-yield phenotyping using drones, and the use of artificial intelligence in data analysis has allowed an increase in crop productivity in recent years. In order to evaluate the factors mentioned in the development of the productivity of bean culture, an experiment will be conducted in a randomized block design, with three repetitions, in a triple factorial scheme 5x2x2, considering five cultivars, two levels of inoculation / co-inoculation, and two levels and fertilization with P2O5. The experiment will be implemented in the irrigated experimental area of FCAT UNESP - Campus de Dracena. Agronomic characteristics of crop development and product attributes will be evaluated. Additionally, high yield phenotyping will be performed with a Phanton 4 Pro V2.0 drone. Periodic flights will be conducted in the crop in order to collect images and make the association of these with the agronomic characteristics measured, using the method of Artificial Intelligence (Artificial Neural Networks). We hope, with the execution of this experiment, to analyze the behavior of the varieties under the conditions evaluated, providing knowledge to the scientific community as well as the popularization of the proposed methodologies, in order to allow the use of such technology in a conscious and sustainable way. (AU)

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