Comparison of Different Image Processing Methods for Segregation of Peanut (Arachis hypogaea L.) Seeds Infected by Aflatoxin-Producing Fungi

Abstract

Fungi such as Aspergillus flavus and Aspergillus parasiticus are molds infecting food and animal feed, are responsible for aflatoxin contamination, and cause a significant problem for human and animal health. The detection of aflatoxin and aflatoxigenic fungi on raw material is a major concern to protect health, secure food and feed, and preserve their value. The effectiveness of image processing, combined with computational techniques, has been investigated to detect and segregate peanut (Arachis hypogaea L.) seeds infected with an aflatoxin producing fungus. After inoculation with Aspergillus flavus, images of peanuts seeds were taken using various lighting sources (LED, UV, and fluorescent lights) on two backgrounds (black and white) at 0, 48, and 72 h after inoculation. Images were post-processed with three different machine learning tools: the artificial neural network (ANN), the support vector machine (SVM), and the adaptive neuro-fuzzy inference system (ANFIS) to detect the Aspergillus flavus growth on peanuts. The results of the study show that the combination of LED light and a white background with ANN had 99.7% accuracy in detecting fungal growth on peanuts 72 h from infection with Aspergillus. Additionally, UV lights and a black background with ANFIS achieve 99.9% accuracy in detecting fungal growth on peanuts 48 h after their infection with Aspergillus.