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Development of a software as a service (SAAS) for soybean integrated disease and pest management by cloud computing and big data mining

Grant number: 15/16014-6
Support Opportunities:Research Grants - Innovative Research in Small Business - PIPE
Duration: May 01, 2016 - January 31, 2017
Field of knowledge:Agronomical Sciences - Agronomy - Plant Health
Principal Investigator:Dreid Cristina Peres Rodero
Grantee:Dreid Cristina Peres Rodero
Host Company:Smartbio Desenvolvimento Tecnológico Ltda
City: Adamantina
Associated researchers: Alfredo Riciere Dias ; Edson Pereira Borges ; Germison Vital Tomquelski ; Marcelo Giovanetti Canteri


Modern Decision Support Systems (DSS) and their respective models have the aim to predict where and when a pathogen or insect will attack one crop and to advise, define and manage its integrated disease and pest management program, respectively. Most of all DSS utilize models based on the interactions of pathogens or insects and their respective hosts under the environmental influence. However, models based on critic thresholds of meteorological parameters determinants for the occurrence, spread and attack intensity of diseases and pests have limited relevance in field scale, since the where obtained in a punctual scale, for a crop growing in a homogeneous condition, almost always, in growing chambers. The risk for the occurrence of disease and pests is a function of the crop traits, disease or pest traits, environment and also the management profile and the technology usage by the farmers. For this reason, the proponent of the present project developed the SMARTBIO IPM SYSTEM® as a universal platform for the development of models and DSS based on historical and real time big data mining of crop traits, disease or pest traits, environment and also the management profile and the technology usage in each farm plot and management group, automatically. The platform was developed in order to analyse, advise, recommend, monitor and manage the integrated disease and pest management in each management group of a farm. Through approved projects by FINEP, CNPq e FAPESP, Smartbio used the universal platform to developed and validate a DSS for the disease and pest management in sugarcane fields, the SMARTBIO IPM SUGARCANE which, due its recognition, intelligence value to the customers levels, it is being used in 35 sugar mills, representing 1 million of hectares. These values were also recognized by professionals working with the soybean crop, stimulating the Fundação Chapadão collaborate with SMARTBIO in the present Project FASE I to develop the "SMARTBIO IPM SOJA: a Software as a Service (SAAS) for Soybean Integrated Disease and Pest Management by Cloud Computing and Big Data Mining". Therefore, the universal platform SMATBIO IPM SYSTEM will be used for the development of the SMARTBIO IPM SOYBEAN. In order to program the routines of the SMARTBIO IPM SOYBEAN, they will be taking in account the ecoinformatics concepts and the new tools for the development of prediction and forecasting model and DSS, as geographic information systems (GIS), fuzzy logic, artificial neural networks, geospatial and temporal big data mining and biostatistics. The parameters for the models, as SMARTBIO SCORES for susceptibility, predisponibility and favorability, specific for each disease and pest in the soybean crop, will be automatically obtained by historical big data mining of 2.000 field experiments conducted by Fundação Chapadão from 2010 to 2015, with different levels of disease and pest attacks and multi management profiles. In the phase one of PIPE, they will be obtained the modules for Soybean Asian Rust (Phakopsora pachyrrizi) e Helicoverpa armigera to be, respectively, the main disease and pest of the soybean crop. The obtained parameters will be optimized and incorporated in the as defaults will be validated in one soybean crop season from December, 2015, to March, 2016 in the Chapadão do Sul, MS, region, in an area of 30 thousands hectares. With the SMARTBIO IPM SOYBEAN, it will be possible to increase the efficiency and adhesion to the soybean disease and pest integrated management and to formulate the most rational, economic and environmental friendly strategy to control with the most suitable weapon at the correct local, time and intensity, contributing with the sustainable and precision agriculture. (AU)

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