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Advanced digital tool for the agricultural risk management

Grant number: 17/19350-2
Support Opportunities:Research Grants - Research Partnership for Technological Innovation - PITE
Duration: December 01, 2018 - May 31, 2021
Field of knowledge:Interdisciplinary Subjects
Convênio/Acordo: IBM Brasil
Principal Investigator:Paulo Estevão Cruvinel
Grantee:Paulo Estevão Cruvinel
Host Institution: Embrapa Instrumentação Agropecuária. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA). Ministério da Agricultura, Pecuária e Abastecimento (Brasil). São Carlos , SP, Brazil
Host Company: IBM Brasil - Indústria, Máquinas e Serviços Ltda
City: Rio de JaneiroSão Carlos
Associated researchers:João de Mendonça Naime ; José Dalton Cruz Pessoa ; José Marcos Garrido Beraldo ; Ladislau Marcelino Rabello ; Silvio Crestana


The State of São Paulo is one of the most prominent states in the Brazilian agricultural sector, being of great relevance to the agribusiness of the country. The objective of this proposal is to develop an Advanced Digital Tool for Agricultural Risk Management, focused on Sao Paulo state, i.e., which can allow the coverage at a local, regional, and national level. It will be developed a robust system for risk management in agriculture based, which can meet the integration of a friendly decision making process based on knowledge and qualified information for farmers in order to anticipate, avoid and react to shocks. The proposed processing services will be implemented on a cloud computing infrastructure to create a cloud computing service, specific to geospatial applications, big data and analytics tools. Further, the processing procedures, information products and geospatial imagery are going to be exposed through web applications built on mapping. The integration of processing and web application allow the users, i.e., also partners, to customize their applications adaptively to the crop they are working with, and based on the cloud infrastructure, content, and diverse data sources. As the data could be searched and accessed through web interfaces and standard APIs, it can be accessed and processed into derived value added products almost immediately following acquisition. Such risk management system will preserve the standard of living of those who depend on farming, strengthen the viability of farm businesses, and provide an environment which supports investment in the farming sector. Besides, the processing system is going to beneficiate smaller companies and researchers that may not have the budget to purchase the required capital equipment. Three basic sources of agricultural risk will be considered: agricultural risks, logistical risk and risk of loss. The options for the delivery of geospatial data include: web map service, tiled map service, web coverage service, and ftp/http file delivery. Currently, no single system is available to expose the required multispectral data to the end user in an efficient and timely manner. The development of such system will improve on existing data access models, which at present are costly, require excessive management, and do not provide access to data quickly enough for agricultural applications. The delivery options are going to facilitate flexible access to data. The selection of delivery will be dependent on the user application. The concept of information agriculture, i.e., agriculture 4.0, will be applied and consideration will be given to the design of a decision support system to store, retrieve and process specific knowledge on the agricultural environment to monitor these key risks associated with biomass production and food security. The proposed research product has significant commercialization potential since the benefits are largely economic since it can reduce agricultural risks, logistical risk and risk of loss, and minimize the food insecurity, among others. Also, the developed tool can be used to reduce false agriculture insurance claims. Secondary benefits include the increasing of food safety and the reduction of environmental impacts, since it profit sustainability. Finally, it will increase the availability of derived agriculture information products for Brazil, as well as increase geospatial knowledge leadership and innovation. The proposed initiative will also foster knowledge transfer between the researchers and the productive sector, including the agricultural industry in Brazil. (AU)

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Scientific publications (8)
(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)
ALVES, GABRIEL M.; CRUVINEL, PAULO E.. Parallel and distributed processing for high resolution agricultural tomography based on big data. MULTIMEDIA TOOLS AND APPLICATIONS, v. N/A, p. 32-pg., . (17/19350-2)
CRUVINEL, PAULO E.; IEEE. Advanced Digital Platform for Agricultural Risk Management. 16TH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2022), v. N/A, p. 8-pg., . (17/19350-2)
CHIUYARI VERAMENDI, WILBUR N.; CRUVINEL, PAULO E.; IEEE. Algorithm For the Countering Maize Plants Based On UAV, Digital Image Processing and Semantic Modeling. 2021 IEEE 15TH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2021), v. N/A, p. 5-pg., . (17/19350-2)
NEVES, RICARDO A.; CRUVINEL, PAULO E.; IEEE. Application of Image Processing and Advanced Intelligent Computing for Determining Stage of Asian Rust in Soybean Plants. 16TH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2022), v. N/A, p. 7-pg., . (17/19350-2)
NEVES, RICARDO A.; CRUVINEL, PAULO E.; IEEE. Model for Semantic Base Structuring of Digital Data to Support Agricultural Management. 2020 IEEE 14TH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2020), v. N/A, p. 4-pg., . (17/19350-2)
NEVES, RICARDO A.; CRUVINEL, PAULO E.; IEEE. Ontology for Structuring a Digital Databases for Decision Making in Grain Production. 2021 IEEE 15TH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2021), v. N/A, p. 7-pg., . (17/19350-2)
LIMA PEREIRA, MAURICIO F.; CRUVINEL, PAULO E.; ALVES, GABRIEL M.; BERALDO, JOSE MARCOS G.; IEEE. Parallel Computational Structure and Semantics For Soil Quality Analysis Based On LoRa and Apache Spark. 2020 IEEE 14TH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2020), v. N/A, p. 5-pg., . (17/19350-2)
BERTOLLA, ALEX B.; CRUVINEL, PAULO E.; IEEE. Band-Pass Filtering for Non-Stationary Noise in Agricultural Images to Pest Control Based On Adaptive Semantic Modeling. 2021 IEEE 15TH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2021), v. N/A, p. 6-pg., . (17/19350-2)

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