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High frequency remote monitoring system for quality management and prediction of agricultural productivity

Grant number: 17/08449-8
Support Opportunities:Research Grants - Innovative Research in Small Business - PIPE
Start date: March 01, 2018
End date: February 29, 2020
Field of knowledge:Physical Sciences and Mathematics - Geosciences - Physical Geography
Principal Investigator:Mateus Vidotti Ferreira
Grantee:Mateus Vidotti Ferreira
Company:IDGeo Inteligência em Dados Geográficos Ltda
CNAE: Atividades de apoio à agricultura
Consultoria em tecnologia da informação
Pesquisa e desenvolvimento experimental em ciências físicas e naturais
City: Piracicaba
Pesquisadores principais:
Camila Barbosa ; Ronan José Campos
Associated research grant:15/22677-8 - Application of orbital cross-sensor techniques for detection and characterization of spatial-temporal changes of relative phytomass in sugar cane parcels, AP.PIPE
Associated research grant(s):19/11567-8 - Sugarcane management platform: remote crop quality monitoring, AP.PIPE
Associated scholarship(s):19/21056-0 - Development of an administrative tool to integrate agricultural resource management information, BP.TT
18/11541-6 - Development of computational algorithms for the implementation of the vegetation growth curve model, anomaly detection and alert system, BP.TT
18/10439-3 - Biometric and spectral monitoring in the field to characterize different conditions of sugarcane development, calibration of vegetation growth models, biomass accumulation and productivity estimation, BP.TT
18/06395-0 - Development of an administrative tool to integrate agricultural resource management information, BP.TT
18/05876-5 - High frequency remote monitoring system for quality management and prediction of agricultural productivity, BP.PIPE

Abstract

Due to increased demand and greater competitiveness of sugarcane production in Brazil, the increase of productivity in the field and the optimization of resources are the key issue to be solved today. Seeking to contribute to maximizing productivity and reducing costs, the present research proposal aims to develop a specialist system for monitoring and supporting the management of raw material quality in the field, based on the integration of a dynamic analysis model of the performance of the Sugarcane growth and productivity prediction, with a diagnostic model and alert of the presence and evolution of plant anomalies in the crop. A combination of multisensor approaches and change detection is proposed in order to increase the frequency of spectral data collection, promoting detailed simulation of canevial evolution, and offering a tool for effective monitoring of agricultural operational efficiency in the field. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
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