Classificação de variedades de gramíneas forrageiras tropicais sob W moderada e severaater Stress using Naïve Bayes and Kernel Density Estimation
DOI:
https://doi.org/10.46420/TAES.e230006Palavras-chave:
machine learning, smart agriculture, physiological variablesResumo
A seleção de gramíneas forrageiras mais adaptadas a condições adversas, como escassez hídrica ou períodos de chuvas secas, é de extrema importância. Principalmente devido às severas mudanças climáticas e à busca por formas de agricultura mais sustentáveis. As gramíneas forrageiras constituem a base da dieta do gado de corte e também são utilizadas como fonte de biocombustíveis, para controle da erosão e melhoria do solo. Este trabalho apresenta uma metodologia de aprendizado de máquina para obtenção de modelos de classificação para nove cultivares forrageiras, sujeitas a estresse hídrico moderado e severo. O algoritmo Naïve Bayes é utilizado em conjunto com o método Kernel Density Estimation para obter as densidades utilizadas nos modelos de classificação. Antes de aprender os modelos, utiliza-se a técnica de validação cruzada agrupada e também a busca em grade para buscar o melhor conjunto de hiperparâmetros. Os melhores resultados de exatidão e precisão são 0,88 e 0,90, respectivamente. Observa-se que o desempenho da classificação depende das cultivares utilizadas nos conjuntos de treinamento e teste. Ao final, as densidades de probabilidade estimadas também são analisadas comparando-as com algumas estatísticas obtidas para cada variável e ambientes de estresse hídrico ou controle. A metodologia proposta é uma abordagem complementar aos métodos estatísticos clássicos. Fornece modelos abstratos para obtenção de informações sobre o ambiente de colheita da cultivar.
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