AFLATOKSİNLİ BİBERLERİN HİPERSPEKTRAL GÖRÜNTÜLERİNİN SINIFLANDIRILMASI İÇİN YENİ YAKLAŞIMLAR

prone to carcinogenic aflatoxin formation during harvesting, production and storage periods. Chemical methods are used for detection of aflatoxins give accurate results, but they are slow, expensive and destructive. In this study, intensity histogram features of hyperspectral images of chili peppers are extracted under halogen and ultraviolet (UV) illumination source. Salient features are selected by using connection weights of artificial neural networks and minimum redundancy maximum relevance techniques. With various topologies of artificial neural networks, effect of data fusion on classification performance is investigated