Marcio Aurelio Soares Santos

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… Exibir mais

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. Exibir menos

Accurate identification of agriculture areas is a key piece in the building blocks strategy of environment and economics resources management. The challenge requires one to deal with landscape complexity, sensors and data acquisition limitations through a proper computational approach to timely deliver accurate information. In this paper, a Machine Learning (ML) based method to enhance the classification process of areas dedicated to seasonal crops (row crops) is proposed. To this objective, a… Exibir mais

Accurate identification of agriculture areas is a key piece in the building blocks strategy of environment and economics resources management. The challenge requires one to deal with landscape complexity, sensors and data acquisition limitations through a proper computational approach to timely deliver accurate information. In this paper, a Machine Learning (ML) based method to enhance the classification process of areas dedicated to seasonal crops (row crops) is proposed. To this objective, a broad exploration of data from Moderate Resolution Imaging Spectro-radiometer sensors (MODIS) was made using pixel time-series combined with time-series similarity metrics. The experiment was performed in Brazil, covered 61% of the total agriculture areas, five different states specifically selected to demonstrate biome differences and the country’s diversity. The validation was made against independent data from EMBRAPA (Brazilian Agriculture Research Corporation), RapidEye Sensor Scene Maps. For the eight tested algorithms, the results were enhanced and demonstrate that the method can rate the classification accuracy up to 98.5%, average value for the tested algorithms. The process can be used to timely monitor large areas dedicated to row crops and enables the application of state of art classification techniques, two levels classification process, to identify crops according to each specific need within the areas. Exibir menos

ESTUDO SOBRE EFICIÊNCIA DO USO DA ÁGUA NO BRASIL: ANÁLISE DO IMPACTO DA
IRRIGAÇÃO NA AGRICULTURA BRASILEIRA E POTENCIAL DE PRODUÇÃO DE ALIMENTOS
FACE AO AQUECIMENTO GLOBAL

Dissertação de Mestrado