Project information
- Category: Strawberry Maturation Assesment
- Data: Strawberry-DS dataset
- Project date: August, 2023
- Project URL: https://doi.org/10.55525/tjst.1342555
Enhancing Strawberry Harvesting Efficiency through Yolo-v7 Object Detection Assessment
This study focuses on automating the detection of strawberry growth stages to prevent premature harvesting and reduce farmer losses, using the Strawberry-DS dataset with 247 greenhouse images covering six growth phases. By applying the YOLO-v7 object detection model, the research achieved strong performance, notably an mAP@.5 of 1.0 for the "turning" phase and overall precision and recall of 0.792 and 0.575, respectively. The results demonstrate reliable, real-time identification of strawberries across various stages, helping to lower labor costs and improve monitoring despite challenges like imbalanced data and unclear labeling.