FoodWorld

The aim of this study was to propose a nondestructive method for evaluating wheat kernels infected by fungi of the genus Fusarium based on the morphological features of kernels and for distinguishing infected kernels from healthy kernels. Images were acquired with a flatbed scanner to determine 59 geometric parameters (linear dimensions and shape factors). Wheat kernels were classified as infected or healthy with the use of Decision trees, Rules, Bayes, Lazy, Meta, and Functions classifiers in WEKA 3.9 software. The classification accuracy of the model based on 59 attributes ranged from 58.12% to 73.37%. The selection of the best attributes shortened the time of the analysis and improved classification results. Linear Forward Selection and MLP classifiers were characterized by the highest classification accuracy.

Practical applications

The infection of cereal grains by fungi of the genus Fusarium is a food security challenge that can compromise the quantity and quality of yields in many agricultural regions. Selected fungi produce mycotoxins with carcinogenic, mutagenic, teratogenic, and immunotoxic effects for humans and animals. In this study, a nondestructive method was applied to evaluate wheat kernels infected by fungi of the genus Fusarium based on the morphological features of kernels and to distinguish infected kernels from healthy kernels. The proposed method can be used for rapid, inexpensive, and effective detection of fungal infections in cereal grain.