Agro 4.0

A data science-based information system for sustainable agroecosystem management

Keywords

Web Applications, Sustentability, Agroecosystems, Data Science, Data Mining, Data analysis, KDD, Intelligent IoT

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Papers

Agro 4.0: A data science-based information system for sustainable agroecosystem management (2020)

One of the solutions for handling and treating the diverse data related to the sustainability of an agroecosystem is the use of Information Systems and Internet of Things. In this work, we adopt a methodology called Indicators of Sustainability in Agroecosystems (Indicadores de Sustentabilidade em Agroecossistemas – ISA), implement an information system based on Internet of Things and apply Data Science and simulation techniques over the gathered data, from 100 real rural properties. As a result, we have developed a set of tools for data collection, processing, visualization, simulation and analysis of the sustainability of a rural property or region, following the ISA methodology. Two experiments were applied on the dataset collected by the tools: environmental change scenarios simulations on targeted agroecosystems to predict how they affect two ISA scores (Soil Fertility and Water Quality) of involved agroecosystems; Evaluation of Feature Selection models searching for subsets of features good enough to predict the two ISA scores for the dataset with a smaller amount of data necessary. We have that with only 7 of the 21 Indicators present in ISA we can identify the level of sustainability in more than 90% of cases, allowing for a new discussion about shrinking the amount of data needed for the computation of ISA, or remodeling the final computation of the Sustainability Index so other Indicators can be more expressive. Users of the solutions developed in this work can identify best practices for sustainability in participating agroecosystems.

Visit Paper (2020)

Agro 4.0: Uma Ferramenta Web para Gestão e Análise da Sustentabilidade em Agroecossistemas. Evandro Caldeira (UFMG), Eugênio Fonseca (UFMG), Leonardo Oliveira (UFMG), Adriano César M. Pereira (UFMG) e Pierre S. Vilela (INAES/FAEMG). (2017)

In this work we present Agro 4.0 , a system that receives data - such as soil quality, water quality, pesticides utilized, farming and agricultural activities -, carefully collected by a rural technician from one or more rural properties and evaluates those properties according to the aspects of an agroecosystem regarding sustainability. The collected data is used by the system to compute indicators used to generate a Sustainability Index for each rural property. The data and indicators also feed statistical models, through data mining techniques, to aid in the extraction of useful information aboutthe relation between the collected data and the indicators obtained. With this information in hand, the rural proprietaries have relevant and quantitative information to aid them in making decisions and taking actions to increase their properties’s sustainability.

Download Paper (2017)

Cite

@article{DAFONSECA2020102068,
  title = "Agro 4.0: A data science-based information system for sustainable agroecosystem management",
  journal = "Simulation Modelling Practice and Theory",
  volume = "102",
  pages = "102068",
  year = "2020",
  note = "Special Issue on IoT, Cloud, Big Data and AI in Interdisciplinary Domains",
  issn = "1569-190X",
  doi = "https://doi.org/10.1016/j.simpat.2020.102068",
  url = "http://www.sciencedirect.com/science/article/pii/S1569190X20300022",
  author = "Eugênio Pacceli Reis [da Fonseca] and Evandro Caldeira and Heitor Soares [Ramos Filho] and Leonardo [Barbosa e Oliveira] and Adriano César Machado Pereira and Pierre Santos Vilela",
  keywords = "Sustainability, Agroecosystems, Intelligent IoT, Data analysis"
}

@article{agro40,
  title     = "Agro 4.0: Uma Ferramenta Web para Gestão e Análise da Sustentabilidade em Agroecossistemas",
  journal   = "Anais do XXIII Simpósio Brasileiro de Sistemas Multimídia e Web: Workshops e Pôsteres",
  year      = "2017",
  month    = "October"
  author    = "Eugênio Fonseca, Evandro Caldeira, Leonardo Oliveira, Adriano C. M. Pereira, Pierre Santos Vilela",
}
              

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