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Remote sensing radar carried by drone

Grant number: 18/00601-8
Support Opportunities:Research Grants - Innovative Research in Small Business - PIPE
Duration: October 01, 2018 - November 30, 2020
Field of knowledge:Engineering - Electrical Engineering - Electrical, Magnetic and Electronic Measurements, Instrumentation
Principal Investigator:Dieter Lubeck
Grantee:Dieter Lubeck
Host Company:Radaz Indústria e Comércio de Produtos Eletrônicos Ltda
CNAE: Desenvolvimento e licenciamento de programas de computador não-customizáveis
Consultoria em tecnologia da informação
Tratamento de dados, provedores de serviços de aplicação e serviços de hospedagem na internet
City: São José dos Campos
Pesquisadores principais:
Laila Fabi Moreira ; Shaila Fabi Moreira
Associated scholarship(s):19/22222-1 - Remote sensing radar carried by drone, BP.PIPE

Abstract

The main objective of this project is the development of a prototype system for mapping and monitoring by radar, to be transported by drone of class 3 (maximum take-off weight of up to 25 kg), parting from a technological demonstrator already developed by T- Jump with its own resources, called DBSS (Drone Borne Survey System) and protected by two patent applications. The prototype already considers resolution No. 419/17, of May 2, 2017, issued by ANAC to regulate civil use of RPA's, popularly called drones, which allows the commercial operation of class 3 drones in up to 120m height over ground without necessity of qualified pilots. The DBSS is the world's first radar imaging system weighing only 1 to 4 kg, depending on the configuration, including its own inertial navigation system and offering simultaneously three-band operation (C, L and P), inclusive interferometric and polarimetric mode that allows monitoring vegetation height, biomass, soil moisture and crop failures, besides the mapping of planimetry and topography, among others. Thus, the DBSS provides unprecedented information to the market, where we see the highest commercial potential in the segment of farming. According to research conducted by UNICAMP's FEAGRI and FEEC faculties, where T-Jump also collaborates, the DBSS application will result in an estimated increase of sugarcane production of up to 30% and a reduction of production costs by at least 20%. Current IBGE data indicate that farming is responsible for 23% of the country's GDP, reinforcing the large potential market of the DBSS and its positive impact on this productivity. For the better operability by the user, the proposal also includes the development of a specific application for the sugar cane production, which will greatly facilitate the feeding of "big data", necessary to guide the decision making of the rural producer in the near future within the context of "e-agriculture", as published in December 2017 by the Coordinator of the Bioenergy Program of the FAPESP Agency, Maria Fernanda Ziegler. The project is very promising. It is expected to sell more than 8,000 DBSS systems by 2024. The financial projections show a positive EBITDA from 2019 on and an internal rate of return of 290% per year in the period from 2018 to 2024. (AU)

Articles published in Pesquisa para Inovação FAPESP about research grant:
Drone-borne radar detects underground ants? nests that threaten eucalyptus plantations 
Articles published in Agência FAPESP Newsletter about the research grant:
Articles published in other media outlets (0 total):
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VEICULO: TITULO (DATA)

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)
ORE, GIAN; ALCANTARA, MARLON S.; GOES, JULIANA A.; OLIVEIRA, LUCIANO P.; YEPES, JHONNATAN; TERUEL, BARBARA; CASTRO, VALQUIRIA; BINS, LEONARDO S.; CASTRO, FELICIO; LUEBECK, DIETER; et al. Crop Growth Monitoring with Drone-Borne DInSAR. REMOTE SENSING, v. 12, n. 4, . (17/19416-3, 18/00601-8)
JHONNATAN YEPES; GIAN ORÉ; MARLON S. ALCÂNTARA; HUGO E. HERNANDEZ-FIGUEROA; BÁRBARA TERUEL. CLASSIFICATION OF SUGARCANE YIELDS ACCORDING TO SOIL FERTILITY PROPERTIES USING SUPERVISED MACHINE LEARNING METHODS. Engenharia Agrícola, v. 42, n. 5, . (18/00601-8, 17/19416-3)
LUEBECK, DIETER; WIMMER, CHRISTIAN; MOREIRA, LAILA F.; ALCANTARA, MARLON; ORE, GIAN; GOES, JULIANA A.; OLIVEIRA, LUCIANO P.; TERUEL, BARBARA; BINS, LEONARDO S.; GABRIELLI, LUCAS H.; et al. Drone-Borne Differential SAR Interferometry. REMOTE SENSING, v. 12, n. 5, . (18/00601-8, 17/19416-3)

Please report errors in scientific publications list by writing to: cdi@fapesp.br.