Olive batches automatic classification in mill reception using computer vision

Olive batches classification is crucial to obtain the best possible oil in milling extracting process. Nowadays, this selection is done manually and it takes place before starting the milling process. This work proposes an automatic classification system of olives batch, based on computer vision, whose goal is the online differentiation of the olives batch according to their quality level. The proposed system has been installed and tested in production condition, working with real lots of olives brought to the mill for the famer. Two full setups have been installed in the factory: before and after the olive washing process. The proposed methodology uses a feature vector of the samples that concatenates the olive image histograms from different colour spaces and the result values of two algorithms used to determine the texture (image entropy and grey level co-occurrence matrix). As classifier, an artificial neural network (ANN) was used. For the experimental validation, 6325 images from 100 batches were analysed showing good classification results (success ratios of 97.1% before the washing stage and 96.4% after).