Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

ABSTRACT: Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most

SUBMITTER: Bisgin H 

PROVIDER: S-EPMC5917025 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature