Drone-borne radar for sugar cane precision agriculture

• Grant number: 17/19416-3
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
• Start date: November 01, 2018
• End date: December 31, 2020
• Field of knowledge: Interdisciplinary Subjects
• Agreement: IBM Brasil
• Principal Investigator: Hugo Enrique Hernández Figueroa
• Grantee: Hugo Enrique Hernández Figueroa
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Company: IBM Brasil - Indústria, Máquinas e Serviços Ltda
• City: Rio de JaneiroCampinas
• Partner institutions: Ministério da Agricultura, Pecuária e Abastecimento (Brasil). Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA). Embrapa Informática Agropecuária
• Pesquisadores principais: Barbara Janet Teruel Mederos
• Associated scholarship(s): 18/12726-0 - Drone-borne radar for sugar cane precision agriculture, BP.MS; 18/14690-2 - Drone-borne radar for sugar cane precision agriculture, BP.MS

Abstract

Precision agriculture is a powerful tool for the increase and cost reduction of the sugar cane production. It represented 27% of the GDP in 2012 and the São Paulo State has the largest production in Brazil. This research project presents a precision agriculture strategy with a novel remote sensing methodology allowing the increase of sugar cane production in at least 30% and a reduction of the production costs in at least 20%. These improvement benchmarks are the success criteria. The approach is based on the use of a drone-borne survey system, DBSS, carrying optical cameras and also a polarimetric, interferometric and multi-band imaging radar. The DBSS can deliver maps of soil moisture and sugar cane classification and biomass additionally to the optical and radar images in a faster, cheaper and better way than the state of the art satellite, aircraft and drone-borne systems available in the market. Software tools will be developed for generating maps of weed, of growth deficit and of production prediction of the sugar cane plantation from the DBSS data. The sugar cane plantation manager will be trained to carry out the corresponding immediate actions. An extensive evaluation of the methodology will be carried out by comparing the DBSS results with the ground true data sets from the field work. Deliverables are the software for generating the set of maps and the precision agriculture procedure for the sugar cane plantation manager. Moreover, all the documentation about the project including the DBSS, the field work and evaluation data will be available as open source codes and open data sets. Two University of Campinas' units: the School of Electrical and Computer Engineering (project leader) and the School of Agricultural Engineering, together with T-Jump Tecnologia Ltda., a small size enterprise and developer of the just launched DBSS, which will carry out the survey and data delivery at no cost. The proposed methodology delivers additional information to the sugar cane production manager and may be complemented by several other information sources. It is understood the global management of the farm or production site is a potential market for IBM, as a supplier of turn-key software solutions. Moreover, the agriculture economy is continuously growing and needing more complex and extensive software packages for overall management. The research topic has the potential of a large scientific impact for being multi-disciplinary, covering the areas of automation, remote sensing, signal and image processing, pattern-recognition, electromagnetic waves interaction with the vegetation and soil, agriculture engineering, geology and production engineering. A wide dissemination and use of the intellectual property is supported, as this work and developed software will be available as an open source. Two master students will be fully engaged. Our results have the potential to be published in highly reputed journals in the fields of electronics and aerospace engineering, electromagnetic waves, remote sensing, agronomy and geology. (AU)

Scientific publications

(7)

(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; SANTOS, ALEXANDRE; UKAN, DANIELE; ZANETTI, RONALD; CAMARGO, MARIANE; OLIVEIRA, LUCIANO P.; HERNANDEZ-FIGUEROA, HUGO E.; IEEE. ANT NESTS DETECTION IN INDUSTRIAL FORESTS BY SAR P-BAND TOMOGRAPHY. 2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), v. N/A, p. 4-pg., 2022-01-01. (17/19416-3, 18/00601-8)

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, MAR 2020. (18/00601-8, 17/19416-3)

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, FEB 2020. (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, 2022-11-14. (18/00601-8, 17/19416-3)

MOREIRA, LAILA; CASTRO, FELICIO; GOES, JULIANA A.; BINS, LEONARDO; TERUEL, BARBARA; FRACAROLLI, JULIANA; CASTRO, VALQUIRIA; ALCANTARA, MARLON; ORE, GIAN; LUEBECK, DIETER; et al. A Drone-borne Multiband DInSAR: Results and Applications. 2019 IEEE RADAR CONFERENCE (RADARCONF), v. N/A, p. 6-pg., 2019-01-01. (17/19416-3, 18/00601-8)

ORE, GIAN; ALCANTARA, MARLON S.; GOES, JULIANA A.; TERUEL, BARBARA; OLIVEIRA, LUCIANO P.; YEPES, JHONNATAN; CASTRO, VALQUIRIA; BINS, LEONARDO S.; CASTRO, FELICIO; LUEBECK, DIETER; et al. Predicting Sugarcane Harvest Date and Productivity with a Drone-Borne Tri-Band SAR. REMOTE SENSING, v. 14, n. 7, p. 24-pg., 2022-04-01. (17/19416-3, 18/00601-8)

GOES, JULIANA A.; CASTRO, VALQUIRIA; BINS, LEONARDO SANT'ANNA; HERNANDEZ-FIGUEROA, HUGO E.; IEEE. 3D Fast Factorized Back-Projection in Cartesian Coordinates. 2020 IEEE RADAR CONFERENCE (RADARCONF20), v. N/A, p. 6-pg., 2020-01-01. (17/19416-3, 18/00601-8)