Hydroponic lettuce defective leaves identification based on improved YOLOv5s

Xin Jin; Haowei Jiao; Chao Zhang; Mingyong Li; Bo Zhao; Guowei Liu; Jiangtao Ji

doi:10.3389/fpls.2023.1242337

Hydroponic lettuce defective leaves identification based on improved YOLOv5s

Front Plant Sci. 2023 Oct 26:14:1242337. doi: 10.3389/fpls.2023.1242337. eCollection 2023.

Authors

Xin Jin^{1

2}, Haowei Jiao¹, Chao Zhang¹, Mingyong Li¹, Bo Zhao³, Guowei Liu⁴, Jiangtao Ji¹

Affiliations

¹ College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China.
² Science and Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang, China.
³ State Key Laboratory of Soil - Plant - Machine System Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing, China.
⁴ Eponic Agriculture Co., Ltd, Zhuhai, China.

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 mAP_0.5 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.

Keywords: BiFPN; EBG_YOLOv5; ECA; GSConv; defect detection.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Key Research and Development China Project (2021YFD2000700), the Postgraduate Education Reform Project of Henan Province (2021SJGLX138Y), and Innovation Scientists and Technicians Team Projects of Henan Provincial Department of Education (23IRTSTHN015).