Proposta de um sistema computacional para monitoramento do ambiente de produção agrícola
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Tese
Date
2020-08-10
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Authors
Santos, Marcio Aurélio Soares
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Omar, Nizam
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Volume Title
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Silva, Leandro Augusto da
Assad, Eduardo Delgado
Gurgel, Ângelo Costa
Valio, Adriana Benetti Marques
Assad, Eduardo Delgado
Gurgel, Ângelo Costa
Valio, Adriana Benetti Marques
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Engenharia Elétrica
Abstract
The identification and geospatial monitoring of areas dedicated to agribusiness is fundamental information in the elaboration of strategies and management of economic
and environmental resources, both of interest to the wider society. In this work, the
objective is to highlight agricultural areas dedicated to seasonal crops, that is, apply a
robust computational method, Machine Learning, and enable the generation of
information about the agricultural areas with the required accuracy and timely. The
challenge requires an approach capable of interacting with different data sources to
timely generate accurate field information. Therefore, it is about dealing with the
complexity of the environment through the lens of sensors and proper modeling. There
are several contributions that aim to meet this demand and in particular, those that
address the extraction of information in large volumes of data, as is the case in this
research which makes use of temporary series extracted from images. Likewise, other
related works share their achievements and improvements using machine learning to
classify agriculture areas and the specifics from each of the studied environments.
The preliminary results are promising, a layer of knowledge that allows the application
of the current techniques and methods to improve information at the culture level has
been generated, a two levels classification process. A comparison of the results with
the combination of similarity metrics to the dataset as additional attributes was made
using the following algorithms: Naive Bayes, Generalized Linear Model, Logistic, Deep
Learning, Decision Tree, Randon Forest, Gradient Boosted tree e Support Vector
Machine. The overall accuracy achieved was between 93.8% e 99.6%, the highest
performance using Boosted Decision Tree algorithms. This information is useful for
future research and also to support the private and public sectors in the monitoring
and spatial planning of food crops in Brazil.
Description
Keywords
aprendizado de maquina , sensoriamento remoto , agricultura , uso do solo , acurácia
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Citation
SANTOS, Marcio Aurélio Soares. Proposta de um sistema computacional para monitoramento do ambiente de produção agrícola. 2020.117 f.. Tese ( Doutorado em Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo, 2020.