PREDIÇÃO DO TEOR DE CLOROFILA EM UMA PLANTAÇÃO DE ARROZ IRRIGADO UTILIZANDO IMAGENS AÉREAS E REDES NEURAIS ARTIFICIAIS
Abstract
O objetivo deste artigo foi desenvolver modelos computacionais para predição do teor de clorofila em uma plantação de arroz irrigado utilizando imagens aéreas e Redes Neurais Artificiais. Através do dispositivo clorofiLOG, foram realizadas medições dos níveis de clorofila nas folhas das plantas do arroz e correlacionados com imagens aéreas coletadas por uma câmera digital portátil, embarcada em um Veículo Aéreo Não Tripulado. As imagens foram coletadas com a aeronave em movimento com velocidade de 2 m/s a uma altura de 50 m sobre o local do experimento. Utilizando Processamento Digital de Imagens, foram gerados 42 índices espectrais, posteriormente selecionados pelo método de seleção por filtro. Os índices foram atribuídos como entradas e as medições de clorofila como saída dos modelos de predição. Desta forma foram desenvolvidos quatro modelos de redes neurais com os respectivos índices de desempenho de R²=0,80, R²=0,7395, R²=0,7775 e R²=0,799. Todos os modelos demonstraram que atingiram ao objetivo desta pesquisa. Desta forma evidencia-se a utilidade destes modelos de predição como ferramentas de auxílio às ciências agronômicas para identificação dos níveis de clorofila na rizicultura. Podem fornecer novas perspectivas na gestão da adubação nitrogenada e melhorias nos custos entre as safras de arroz.
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