Hydroponic lettuce defective leaves identification based on improved YOLOv5s

Abstract

Achieving intelligent detection of defective leaves of hydroponic lettuce after harvesting is of great significance for ensuring the quality and value of hydroponic lettuce. In order to improve the detection accuracy and efficiency of hydroponic lettuce defective leaves, firstly, an image acquisition system is designed and used to complete image acquisition for defective leaves of hydroponic lettuce. Secondly, this study proposed EBG_YOLOv5 model which optimized the YOLOv5 model by integrating the attention mechanism ECA in the backbone and introducing bidirectional feature pyramid and GSConv modules in the neck. Finally, the performance of the improved model was verified by ablation experiments and comparison experiments. The experimental results proved that, the Precision, Recall rate and mAP0.5 of the EBG_YOLOv5 were 0.1%, 2.0% and 2.6% higher than those of YOLOv5s, respectively, while the model size, GFLOPs and Parameters are reduced by 15.3%, 18.9% and 16.3%. Meanwhile, the accuracy and model size of EBG_YOLOv5 were higher and smaller compared with other detection algorithms. This indicates that the EBG_YOLOv5 being applied to hydroponic lettuce defective leaves detection can achieve better performance. It can provide technical support for the subsequent research of lettuce intelligent nondestructive classification equipment.