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.

处理和处理与农业生态系统可持续性相关的各种数据的解决方案之一是使用信息系统和物联网。在这项工作中，我们采用一种称为“农业生态系统可持续性指标”的方法（农业生态系统指标）– ISA），实施基于物联网的信息系统，并将数据科学和模拟技术应用于收集的来自100个实际农村地区的数据。结果，我们按照ISA方法开发了一套工具，用于数据收集，处理，可视化，模拟和分析农村财产或地区的可持续性。通过该工具收集的数据集进行了两个实验：针对目标农业生态系统的环境变化情景模拟，以预测它们如何影响所涉及农业生态系统的两个ISA分数（土壤肥力和水质）；对特征选择模型进行评估，以寻找足以预测数据集的两个ISA分数的特征子集，而所需的数据量则更少。我们认为，只有ISA中存在21个指标中的7个，我们才能在90％以上的情况下确定可持续性水平，从而可以进行新的讨论，以减少ISA计算所需的数据量，或者对最终模型进行重塑。计算可持续性指数，以便其他指标更具表现力。在这项工作中开发的解决方案的用户可以确定参与农业生态系统可持续性的最佳做法。