Strawberry ripeness detection based on YOLOv8 algorithm fused with LW-Swin Transformer

Abstract

Identifying the ripeness of strawberries can be challenging due to their complex growth environment, interference from light intensity, and shading caused by strawberry aggregation. To address these issues, this study aims to develop an algorithm for accurately detecting and classifying ripe strawberries. This study proposed a novel LS-YOLOv8s model for detecting and grading the ripeness of strawberries, which is based on the YOLOv8s deep learning algorithm and incorporates the LW-Swin Transformer module. To improve the performance of the model, two new random variables were introduced in the contrast enhancement process to control the enhancement effect. The dataset was expanded from 1089 to 7515 images, which increased the diversity of the data and reduced the risk of over fitting the model. Additionally, the Swin Transformer module was added to the TopDown Layer2 during the feature fusion stage to capture long distance dependencies in the input data and improve the generalization capability of the model with the use of a multi-headed self-attention mechanism. Finally, a more efficient feature fusion network was achieved by introducing a residual network with learnable parameters and scaled normalization into the original residual structure of the Swin Transformer. To evaluate the effectiveness of LS-YOLOv8s for strawberry ripeness detection, we collected a dataset of strawberry images from a strawberry planting base. The dataset was split using the 5-fold cross-validation approach, which improved the model evaluation process. Experimental results showed that LS-YOLOv8s better than other models, with a 1.6 %, 33.5 %, and 3.4 % improvement in mAP0.5 on the validation set compared to YOLOv5s, CenterNet, and SSD, respectively. Moreover, LS-YOLOv8s achieved better detection precision and speed than YOLOv8m with only approximately 51.93 % of the number of parameters used, achieving 94.4 % detection precision and 19.23fps detection speed, improving by 0.5 % and 6.56fps, respectively. The LS-YOLOv8s model can provide reliable theoretical support for detecting strawberry targets, evaluating their ripeness, and automating the strawberry picking process for orchard management.