An Approach for Identifying of Fusarium Infected Maize Grains by Spectral Analysis in the Visible and Near Infrared Region, SIMCA Models, Parametric and Neural Classifiers

An Approach for Identifying of Fusarium Infected Maize Grains by Spectral Analysis in the Visible and Near Infrared Region, SIMCA Models, Parametric and Neural Classifiers

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An approach for identifying of Fusarium infected single maize grains based on diffuse reflectance in visible and near infrared region is proposed in the paper.Spectral characteristics were collected in the range 400-2500 nm in moondrop quark steps of 2 nm.Soft independent modeling of class analogy (SIMCA) is used for data vacuum pro vst processing.Maize grains classification is based on SIMCA classifier and Probabilistic neural network (PNN).Recognition accuracy which is achieved for both classes of grains is respectively 99.

89% for healthy, and 93.7% for infected.