Lightweight Recognition Method for Korla Pear Based on NanoDet-Plus

Abstract

This study aims to tackle the challenge of identifying Korla pears in real orchard environments by developing a highly accurate and lightweight target detection model. This model is designed to assist with automated harvesting and yield estimation. We improved the feature extraction capabilities of the NanoDet-Plus framework by incorporating MobileNetV3 as the backbone network. Additionally, we used the DBSCAN clustering algorithm to segment and compile a high-quality dataset of pears. The improved model achieved mAP@0.5 and mAP@.5:.95 scores of 97.9% and 85.1% on the test datasets, representing improvements of 0.3% and 0.5% over the original model. After post-quantization compression, the model's size was reduced by 94.2%, allowing it to perform stable detection on devices with limited computing power. This network balances high accuracy with low resource consumption, making it suitable for recognizing pears in complex orchard environments and providing a practical lightweight solution for smart agriculture applications.