Classifying Healthy and Fungal Infected-Pistachio Kernel by Thermal Imaging Technology

Abstract

Thermal imaging has been considered as a new beneficial technique for inspectional aims in agriculture and also in food safety and food control. In this research, fungal infection caused by KK11 and R5 isolates of Aspergillus flavus fungi was detected by thermal imaging technique. Seven infection stages were considered to be identified by the technique. The features from acquired thermograms from healthy and infected pistachio kernels were extracted, selected, and then classified based on quadratic discriminant analysis and artificial neural network methods in MATLAB 2010 environment. The results showed that thermal imaging successfully can classify healthy and fungal-infected pistachio kernels without considering the isolate type with 99.00% accuracy. This technique separated infection stage one from other stages with accuracy of 86.30%.