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Technical and commercial development of mobile web APP to increase the efficiency of the integrated management of soybean pests and diseases through cloud computing and Ag big data mining

Grant number: 17/07841-1
Support Opportunities:Research Grants - Innovative Research in Small Business - PIPE
Duration: December 01, 2017 - November 30, 2019
Field of knowledge:Agronomical Sciences - Agronomy - Plant Health
Convênio/Acordo: FINEP - PIPE/PAPPE Grant
Principal Investigator:Éder Antônio Giglioti
Grantee:Éder Antônio Giglioti
Host Company:Smartbio Desenvolvimento Tecnológico Ltda
CNAE: Desenvolvimento de programas de computador sob encomenda
Desenvolvimento e licenciamento de programas de computador customizáveis
City: Adamantina
Associated researchers:Carlos Eduardo Aparecido Schults ; Cristiano Fagundes ; Donavan Peres Rodero ; Dreid Cristina Peres Rodero ; Fabio Costa dos Santos ; Fabricio Leonardo Grego ; Marcelo Giovanetti Canteri


Although enormous effort for the Integrated Pest and Disease Management (IPDM), farmers still suffer with unnecessary expenses by the inadequate use of pesticides or with huge potential of yield losses provided by the attack of fungi, viruses and insects that can reduce production by as much as 70%. This difficulty is enforced by the complexity of the interaction between pathogen or insect with the plant-environment-man. To overcome this difficulty and provide greater efficiency in the processes of the IPDM and, consequently, increase productivity, the Mobile Web App SMARTBIO IPM SOYBEAN was developed with the support of the PIPE phase I of FAPESP (Process: 2015/16014-6). With this system, farmers can predict where and when a pathogen or insect is going to attack their crops and, with this information, advise, size, recommend, schedule and manage all activities of the IPDM most suited to each plot or grid of their farms. This is achieved because the SMARTBIO technology allows that more than one billion data are stored, processed and analyzed in quadrillions of possible combinations for decision-making in real time, through models of forecasting and decision-making platforms, as technologies of geographic information system (GIS), fuzzy logic, neural networks, geospatial and temporal data mining and Biostatistics that are carried out by cloud computing, connectivity (web) and mobility (mobile). This technology is already used successfully in more than 1,500,000 hectares of sugar cane and now can contribute to greater efficiency in the pests and diseases control of soybean crops. To turn this possible, as a result of the FAPESP-PIPE of the High-Tech Entrepreneurial Training Program, the SMARTBIO proposes this project to PIPE/PAPPE PHASE 3 to the "Technical and Commercial Development of Mobile Web App to Increase the Efficiency of the Integrated Management of Soybean Pests and Diseases Through Cloud Computing and Ag Big Data Mining". To this end, the integration of real-time and historical data and configuration, adaptation and validation of the SMARTBIO IPM SOYBEAN scientific and legal and technological and operational conditions standards in four Pilot and Demonstration Units of 30 thousand hectares distributed in major soybean producing States. Interviews with producers also will define the tactics of pricing and marketing models. Finally, through the planning and execution of a Marketing Plan, will be carried out activities since the development of strategies to the final product showcase in events, workshops, prints, electronic media and social networks, so that the technology can be inserted into the market aggressively, impressive and successfully. If farmers fail to lose and/or save at least R$ 200 per hectare, the earning potential for the 32 million hectares cultivated with soybeans in Brazil reaches a value of R$ 6.4 billion each year. In addition to economic gain, the SMARTBIO IPM SOYBEAN will promote reduction of environmental impacts by providing the correct use of chemical pesticides and stimulate the adoption of biological control, promoting sustainable digital agriculture. (AU)

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