Evaluation of Deep Neural Network Architectures and Image Datasets for Olive Fruit Detection

Josip Music, Mirjana Bonkovic, Toma Sikora, Vladan Papic

Abstract: Accurate fruit yield estimation is increasingly recognized as a vital component of sustainable agricultural practices, particularly in economically and culturally significant crops like olives. As the global demand for olive oil rises, so does the need for precise yield forecasting, which can lead to better resource allocation and enhanced management practices. The advent of machine learning (ML), particularly deep neural networks (DNNs) and computer vision, has revolutionized agricultural yield estimation practices. Thus, in this paper, we explore several state-of-the-art DNN architectures and their performance on two databases: our custom one (with 11,227 olive instances) and a database available from the literature (with 245,089 olive instances). Obtained results (mAP50 ranging from 68% to 95% depending on the used architecture) demonstrate the applicability of the DNN-based approaches in the complex and difficult task of olive detection from high-resolution images, while highlighting the importance of database size and quality (diversity) on DNN's final performance.

External IDs:dblp:conf/softcom/MusicBSP25