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Data fusion in agriculture: resolving ambiguities and closing data gaps. Abstract. Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as te... ... Repository: BDPA Publication year: 2022
The Research Data Alliance Interest Group on Agricultural Data: supporting a global community of practice. This chapter reflecs on the lessons learnt from the IGAD community of practive in its attempts to include new voices around the world. Repository: BDPA Publication year: 2023
Multilevel data fusion for the internet of things in smart agriculture. Repository: BDPA Publication year: 2020
Building a community-based FAIR metadata schema for Brazilian agriculture and livestock trading data. In this paper, we discuss how we are using metadata schemas and controlled vocabularies to improve interoperability between Brazilian agriculture and livestock trading data providers. A new metadata s... ... Repository: BDPA Publication year: 2022
Improving regional coverage of agricultural data in a background database. Abstract: The creation of reliable, consistent and transparent regionalized LCIs represents a core purpose of the Sustainable Recycling Industries-Life Cycle Inventories (SRI-LCI) project. SRI-LCI was... ... Repository: BDPA Publication year: 2019
A prospective study on the application of Data Science in agriculture. A quantidade e diversidade de dados disponíveis têm o potencial de causar profundas transformações na maneira que se realiza pesquisa e se propõe inovações na agricultura. Na chamada era do Petabyte,... ... Repository: BDPA Publication year: 2017
Remote sensing and kriging with external drift to improve sparse proximal soil sensing data and define management zones in precision agriculture. The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number o... ... Repository: BDPA Publication year: 2023
Feature engineering vs. extraction: clustering Brazilian municipalities through spatial panel agricultural data via autoencoders. This article compares the clustering of Brazilian municipalities according to their agricultural diversity using two approaches, one based on feature engineering and the other based on feature extract... ... Repository: BDPA Publication year: 2022
Mapping agricultural intensification in the Brazilian savanna: a machine learning approach using harmonized data from Landsat Sentinel-2. Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pre... ... Repository: BDPA Publication year: 2023
Feature extraction of spatial panel data with autoencoders for clustering the Brazilian agricultural diversity. ABSTRACT - Brazilian agricultural production presents a high degree of spatial diversity, which challenges designing territorial public policies to promote sustainable development. This article propos... ... Repository: BDPA Publication year: 2022

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