Active Learning Method for Classifying The Mold Contaminated Figs

Gunes A., Bilgi A. S., Ortac G., Kalkan H., TAŞDEMİR K.

24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Turkey, 16 - 19 May 2016, pp.1169-1172 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2016.7495953
  • City: Zonguldak
  • Country: Turkey
  • Page Numbers: pp.1169-1172
  • Süleyman Demirel University Affiliated: Yes


Turkey is the major producer of dried figs in the world and lives mold- based problems on dried figs production. Among the various types of molds, Aspergillus niger type molds are the most common one on the figs which is called "black mold" in food industry. In fig processing industry, black mold infected figs are manually detected by workers using visual observation and also by a native method which is called "nailing". The nailing method is labor intensive and it carries the risk of transmitting the black mold to the sound figs. In this study, a hyperspectral imaging system is proposed to detect the black-mold infected figs by using active learning. In food safety application, obtaining a number of labeled samples from opposite classes is usually required to train the classifier. However, labeling the samples before training the classifier includes the risk of labeling the samples, which do not carry significant information for the classifier. In this study, in contrast to labeling before training, an active learning scheme is used to detect the samples which are relevant for the classifier are detected and labeled. Using the proposed system, a classifier with higher accuracy is trained with respectively less number of samples.