Redes Neurais artificiais e Sistema Adaptativo de Inferência Neuro Fuzzy para análise e previsão da produtividade do trigo
DOI:
https://doi.org/10.32404/rean.v10i3.7553Palavras-chave:
Rendimento da colheita, Correlação, ANN, ANFISResumo
O presente estudo avaliou a previsão do rendimento das culturas de trigo no distrito de Bagalkot, do Estado de Karnataka, India. O estudo teve como objetivo fornecer previsões de rendimento das colheitas para ajudar os agricultores a otimizar suas estratégias de cultivo e comercialização. O modelo usou várias variáveis independentes tais como temperatura, humidade do ar e recursos hídricos para prever o crescimento no rendimento das culturas de trigo. O trabalho se desenvolveu e testou dois modelos diferentes: Modelo de Rede Neural Artificial (Artificial Neural Network – ANN) e Sistema de Interferência Neuro-fuzzy Adaptativo (Adaptive Neuro-fuzzy Interference System - ANFIS) a fim prever o crescimento do rendimento das culturas com base nas variáveis independentes selecionadas. O modelo ANFIS foi particularmente interessante, pois pôde prever um mapeamento entre os parâmetros de entrada e saída, os quais podem ser úteis para compreender a relação entre diferentes variáveis. ANFIS foi considerado um modelo de predição melhor que o modelo ANN, com uma porcentagem de erro variando de 0-3%. De maneira geral, o trabaho destacou a importância das previsões do rendimento das culturas e os potenciais benefícios que as simulações podem gerar para os agricultores e para o setor agrícola em geral.
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