High resolution remote sensing for digital agriculture

• Grant number: 21/06029-7
• Support Opportunities: Research Projects - Thematic Grants
• Start date: August 01, 2022
• End date: July 31, 2027
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Geodesy
• Principal Investigator: Antonio Maria Garcia Tommaselli
• Grantee: Antonio Maria Garcia Tommaselli
• Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil
• Pesquisadores principais: Aluir Porfírio Dal Poz ; Nilton Nobuhiro Imai ; Rouverson Pereira da Silva
• Associated researchers: Adilson Berveglieri ; Ana Paula Marques Ramos ; Bruno Sérgio Vieira ; David Ferreira Lopes Santos ; David Luciano Rosalen ; Edemar Moro ; Fabio Fernando de Araujo ; Fernanda Sayuri Yoshino Watanabe ; Gelci Carlos Lupatini ; George Deroco Martins ; João Carlos Cury Saad ; Maria de Lourdes Bueno Trindade Galo ; Mauricio Galo ; Milton Hirokazu Shimabukuro ; Odair Aparecido Fernandes ; Paulo de Oliveira Camargo ; Raul Queiroz Feitosa ; Renato César dos Santos
• Associated research grant(s): 24/04106-2 - 2024 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, AR.EXT; 22/11647-4 - Multi-user equipment approved in grant 2021/06029-7: Terrestrial Laser Scanner FARO Focus Premium 70, AP.EMU
• Associated scholarship(s): 24/15322-8 - EVALUATION OF MICROBIAL ACTIVITY AND NEMATODE COMMUNITY IN AN INTEGRATION SYSTEM FOR LIVESTOCK CROP, BP.IC; 24/16495-3 - Estimation of Soybean Productivity, Pasture Mass, and Chemical Composition through Remote Sensing, BP.TT; 23/14756-1 - CORRESPONDENCE DETERMINATION AND RELATIVE ORIENTATION OF POINT CLOUDS ACQUIRED BY TERRESTRIAL LASER SYSTEM, BP.MS; + associated scholarships 23/12653-0 - APPLICATION OF MACHINE LEARNING ALGORITHMS AND REMOTE SENSING IN PREDICTING PEANUT HARVEST LOSS, BP.IC; 23/13500-3 - MAPPING OF WEEDS IN SWEET POTATO CROPS USING HIGH SPATIAL RESOLUTION IMAGES, BP.IC; 23/11289-3 - Classification of nematode infested areas based on a machine learning algorithm using Planet multispectral images, BP.IC; 23/14041-2 - Estimating productivity of potatoes using digital agriculture tools, BP.PD; 23/15204-2 - Registration techniques for multiespectral images collected by a mobile terrestrial system., BP.IC; 23/08119-9 - HIGH-RESOLUTION REMOTE SENSING FOR POTATO CROP MONITORING, BP.IC; 23/11130-4 - Financial Performance of Agricultural Production Systems with Physical and Digital Systems, BP.TT; 23/04806-1 - EVALUATION OF MICROBIAL ACTIVITY AND NEMATODE COMMUNITY IN AN INTEGRATION SYSTEM FOR LIVESTOCK CROP AND ITS RELATIONSHIP WITH REMOTE SENSING, BP.IC; 23/02772-2 - Detection of orange plantation lines using high-resolution orthomosaic and convolutional neural network, BP.IC; 22/16084-8 - A framework for high-resolution remote sensing in tomato crop upon minicomputer and cloud computing, BP.DR; 23/01099-2 - Processing multispectral images and LASER scanning data collected at close range, BP.TT; 22/12750-3 - Survey and processing of aerial and terrestrial remote sensing data in areas of grass and soy, BP.TT; 22/14168-0 - Illumination and acquisition geometry influence on the RPA - Remotely Piloted Aircraft Multispectral image reflectance factor - An experimental evaluation, BP.IC; 22/12681-1 - Generation of multispectral point clouds by a mobile terrestrial system, BP.IC - associated scholarships

Abstract

Sustainable agriculture in 21st century will face major challenges being mandatory to assimilate new technologies to increase agricultural productivity and sustainability. digital agriculture is a key technology to face those challenges that includes multiresolution remote sensing, accurate geodetic positioning, geographic information systems, and artificial intelligence. The main aim of this project 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 with state of art machine learning techniques. Accuracy of multitemporal geospatial data will be a special concern as well as the use of artificial intelligence, intelligence that will support bioeconomic model for measure financial and production impact. In particular, this project will also strength cooperation with international players which will receive students and staff for one-year internships or short visits, deliver tutorials and post-graduate levels and promote joint researches. (AU)

Scientific publications

(6)

(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)

MORIYA, ERIKA AKEMI SAITO; IMAI, NILTON NOBUHIRO; TOMMASELLI, ANTONIO MARIA GARCIA; HONKAVAARA, EIJA; ROSALEN, DAVID LUCIANO. Design of Vegetation Index for Identifying the Mosaic Virus in Sugarcane Plantation: A Brazilian Case Study. AGRONOMY-BASEL, v. 13, n. 6, p. 20-pg., 2023-06-01. (13/50426-4, 21/06029-7)

WERNECK ORLANDO, VINICIUS SILVA; MARTINS, GEORGE DEROCO; FRAGA JUNIOR, EUSIMIO FELISBINO; MARRA, ALINE BARROCA; PEREIRA, FERNANDO VASCONCELOS; TRINDADE GALO, MARIA DE LOURDES BUENO. POTENTIAL OF MULTISPECTRAL IMAGES TAKEN BY SENSORS EMBEDDED IN UAVS FOR MONITORING THE COFFEE CROP IRRIGATION. GEOSPATIAL WEEK 2023, VOL. 10-1, v. N/A, p. 6-pg., 2023-01-01. (21/06029-7)

GARCIA, THAISA ALINE CORREIA; TOMMASELLI, ANTONIO MARIA GARCIA; CASTANHEIRO, LETICIA FERRARI; CAMPOS, MARIANA BATISTA. A photogrammetric approach for real-time visual SLAM applied to an omnidirectional system. PHOTOGRAMMETRIC RECORD, v. 39, n. 187, p. 23-pg., 2024-04-30. (21/06029-7)

BERVEGLIERI, ADILSON; IMAI, NILTON NOBUHIRO; WATANABE, FERNANDA SAYURI YOSHINO; TOMMASELLI, ANTONIO MARIA GARCIA; EDERLI, GLORIA MARIA PADOVANI; DE ARAUJO, FABIO FERNANDES; LUPATINI, GELCI CARLOS; HONKAVAARA, EIJA. Remote Prediction of Soybean Yield Using UAV-Based Hyperspectral Imaging and Machine Learning Models. AGRIENGINEERING, v. 6, n. 3, p. 19-pg., 2024-09-01. (21/10823-0, 21/06029-7)

MARRA, A. B.; GALO, M. L. B. T.; TONOLO, F. GIULIO; SANO, E. E.; ORLANDO, V. S. W.. BURNT AREAS SEMANTIC SEGMENTATION FROM SENTINEL DATA USING THE U-NET NETWORK TRAINED WITH SEMI-AUTOMATED ANNOTATIONS. 39TH INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT ISRSE-39 FROM HUMAN NEEDS TO SDGS, VOL. 48-M-1, v. N/A, p. 7-pg., 2023-01-01. (21/06029-7)

MACHADO, M. V.; TOMMASELLI, A. M. G.. UAV-LIDAR BORESIGHT ESTIMATION USING VIRTUAL CONTROL POINTS: A CASE STUDY. GEOSPATIAL WEEK 2023, VOL. 10-1, v. N/A, p. 8-pg., 2023-01-01. (13/50426-4, 21/06029-7)