HIGH RESOLUTION REMOTE SENSING FOR DIGITAL AGRICULTURE

• Grant number: 25/00902-1
• Support Opportunities: Scholarships in Brazil - Scientific Journalism
• Start date: June 01, 2025
• End date: May 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Geodesy
• Principal Investigator: Antonio Maria Garcia Tommaselli
• Grantee: Marco Vinicius Trindade Ropelli
• Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil
• Institution abroad: Universidade de São Paulo (USP), Brazil
• Associated research grant: 21/06029-7 - High resolution remote sensing for digital agriculture, AP.TEM

Abstract

The main objective of this project, together with the appropriate training of the journalist, is the scientific communication of the results achieved in the thematic project HIGH-RESOLUTION REMOTE SENSING FOR DIGITAL AGRICULTURE. This communication is aimed at other researchers, rural producers and the general public which requires multiple communication approaches, which will be specific and applied by the candidate. This communication is essential, given the challenges that agriculture has been facing, which require the assimilation and development of new technologies to increase productivity in a sustainable way. Digital agriculture is a key technology that faces challenges that include multiresolution remote sensing, accurate geodetic positioning, geographic information systems, and artificial intelligence. The main aim of the thematic project to which this proposal is connected is to study the use of multitemporal, multi-resolution, and multi-platform images collected from multiple sensors in various resolutions, various types of sensors: orbital, aerial high-resolution systems with UAVs, static ground and mobile will be used. The results will include the development of a terrestrial multi-sensor platform and the corresponding calibration processes, integrated orientation and generation of multispectral point clouds, which will be classified to generate information on the existence of pathogens, nutritional deficiencies, structural changes and fruit quantification or individual plants, being used in this project for Citrus, Coffee and other crops. The focus of this project will be the acquisition and data processing, integrating data coming from multiple sensors to produce accurate and dense multitemporal multiband 3D data. This data will be analysed and classified using state-of-the-art machine learning techniques. The accuracy of multitemporal geospatial data will be a special concern, as well as the use of artificial intelligence, which will support bioeconomic models for measuring financial and production impact.