  This study proposed a YOLOv5-based custom object detection model to detect
strawberries in an outdoor environment. The original architecture of the
YOLOv5s was modified by replacing the C3 module with the C2f module in the
backbone network, which provided a better feature gradient flow. Secondly, the
Spatial Pyramid Pooling Fast in the final layer of the backbone network of
YOLOv5s was combined with Cross Stage Partial Net to improve the generalization
ability over the strawberry dataset in this study. The proposed architecture
was named YOLOv5s-Straw. The RGB images dataset of the strawberry canopy with
three maturity classes (immature, nearly mature, and mature) was collected in
open-field environment and augmented through a series of operations including
brightness reduction, brightness increase, and noise adding. To verify the
superiority of the proposed method for strawberry detection in open-field
environment, four competitive detection models (YOLOv3-tiny, YOLOv5s,
YOLOv5s-C2f, and YOLOv8s) were trained, and tested under the same computational
environment and compared with YOLOv5s-Straw. The results showed that the
highest mean average precision of 80.3% was achieved using the proposed
architecture whereas the same was achieved with YOLOv3-tiny, YOLOv5s,
YOLOv5s-C2f, and YOLOv8s were 73.4%, 77.8%, 79.8%, 79.3%, respectively.
Specifically, the average precision of YOLOv5s-Straw was 82.1% in the immature
class, 73.5% in the nearly mature class, and 86.6% in the mature class, which
were 2.3% and 3.7%, respectively, higher than that of the latest YOLOv8s. The
model included 8.6*10^6 network parameters with an inference speed of 18ms per
image while the inference speed of YOLOv8s had a slower inference speed of
21.0ms and heavy parameters of 11.1*10^6, which indicates that the proposed
model is fast enough for real time strawberry detection and localization for
the robotic picking.
