Automated classification of olive fruit for enhanced olive oil production using computer vision

Abstract

The olive oil industry plays a significant role in the global agricultural economy, with the quality of olive oil greatly dependent on the quality and ripeness of the olives used in its production. Accurate and efficient sorting and classification of olive fruit are crucial steps in optimizing olive oil yield and quality. In this scientific project, we propose a novel approach to automate the classification of olive fruit based on their ripeness and quality using computer vision techniques. The visual system is composed of a segmentation and classification deep network, based on YOLO architecture. In practice despite the processing unexpected foreign objects may be present as well (e.g. leaves, twigs etc), which may lead to erroneous classification to one of the existing classes. A classification problem is therefore defined. The experimental results validate the utility of the approach with high classification accuracy based on expert annotation and demonstrate high detection rates for outlier objects. The speed of the system ensures a high production throughput.