Deep Learning-based Lightweight Models for Bactrocera Oleae Detection

Abstract

The detection of Bactrocera oleae (BO), an invasive pest that poses a serious threat to olive tree cultivation, is crucial for ensuring the quality and quantity of olive oil production. This study explores the application of deep learning with lightweight models for BO detection in olive trees. The DIRT Dataset, consisting of 841 images obtained from traps, was used for training and evaluation. Different combinations of Faster R-CNN and SSD models with various backbone architectures, including EfficientNet B0, MobileNet v3, ResNet-18, and ResNet-50, were assessed. The combination of Faster R-CNN with ResNet-18 achieves comparable results to Faster R-CNN with ResNet-50 but with lower latency and a reduced parameter count. These findings highlight the potential of deep learning-based object detection models for BO detection in olive trees, offering efficient and accurate identification of infestations.