LDC-PP-YOLOE: a lightweight model for detecting and counting citrus fruit

Abstract

In the citrus orchard environment, accurate counting of the fruit, and the use of lightweight detection methods are the key presteps to automate citrus picking and yield estimations. Most high-precision fruit detection models based on deep learning use complex models with devices that require high quantities of computational resources and memory. Devices with limited resources cannot meet the requirements of these models. Thus, to overcome this problem, we focus on creating a lightweight model with a convolutional neural network. In this research, we propose a lightweight citrus detection model based on the mobile device LDC-PP-YOLOE. LDC-PP-YOLOE is improved based on PP-YOLOE by using localized knowledge distillation and CBAM, with a mAP@0.5 of 88\(\%\), mAP@0.95 of 51.3\(\%\), params of 8 M and speed of 0.34 s, respectively. The performance of LDC-PP-YOLOE was compared against commonly used detectors and LDC-PP-YOLOE’s mAP@0.5 was 2.5, 6.9 and 16.3\(\%\), and was 4.3\(\%\) greater than Faster R-CNN, YOLOX-s and PicoDet-L, respectively. LDC-PP-YOLOE achieved an RMSE of 8.63 and an MSE of 5.27 compared to the ground truth on citrus applications. In addition, we used apple and passion fruit datasets to verify the generalization of the model; the mAP@0.5 is improved by 1 and 0.7\(\%\). LDC-PP-YOLOE can be used as a lightweight model to help growers track citrus populations and optimize citrus yields in complex citrus orchard environments with resource-limited equipment. It also provides a solution for lightweight models.