SE-YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and Vegetables

Abstract

Weeds in the field affect the normal growth of lettuce crops by competing with them for resources such as water and sunlight. The increasing costs of weed management and limited herbicide choices are threatening the profitability, yield, and quality of lettuce. The application of intelligent weeding robots is an alternative to control intra-row weeds. The prerequisite for automatic weeding is accurate differentiation and rapid localization of different plants. In this study, a squeeze-and-excitation (SE) network combined with You Only Look Once v5 (SE-YOLOv5x) is proposed for weed-crop classification and lettuce localization in the field. Compared with models including classical support vector machines (SVM), YOLOv5x, single-shot multibox detector (SSD), and faster-RCNN, the SE-YOLOv5x exhibited the highest performance in weed and lettuce plant identifications, with precision, recall, mean average precision (mAP), and F1-score values of 97.6%, 95.6%, 97.1%, and 97.3%, respectively. Based on plant morphological characteristics, the SE-YOLOv5x model detected the location of lettuce stem emerging points in the field with an accuracy of 97.14%. This study demonstrates the capability of SE-YOLOv5x for the classification of lettuce and weeds and the localization of lettuce, which provides theoretical and technical support for automated weed control.

Keywords

• YOLOv5
• attention mechanism
• transfer learning
• deep learning
• weed identification
• SVM