Detection of Pits in Olive Using Hyperspectral Imaging Data

Abstract

The unknowing presence of pits in olives is an undesirable and even dangerous experience that affects overall quality of products contain olives or serving dishes in salad bars, airline meals, and cocktail lounges. Therefore, detection for removal of pits and pit fragments are very important for maintaining a high quality of products/services. The contributions of the paper are a) new hyperspectral data captured in a specific range of the wavelengths that are suitable for detecting olives with pits inside; b) developing a classification method, that is the CNN classifier that yields a high accuracy of pit detection. We have collected spectral responses of Manzanilla (black and green) and Gemlik (black) olives in the 400 nm-900 nm spectrum. Olives have been classified using 1D convolutional neural networks (1D-CNN) with 99.5% and 97.69% of classification accuracies of pitted and whole olives for training and test sets, respectively. Further boosting of the accuracy up to 98.27% on the test dataset has been attained by CNN with a dropout layer. As expected, the CNN attained a better performance than those of the Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LA) classifiers.

Keywords

• Olive pit
• hyperspectral imaging
• CNN
• food safety
• non-destructive test
• RGB