基于DW-YOLOv7-tiny的荠菜种子出芽检测方法OA

To enhance efficiency and accuracy of germination detection for ultra-small seed and meet demand for automation,shepherd's purse seed were selected as research subject.Through shepherd's purse seed germination experiments'image analysis,a YOLOv7-tiny-based seed germination detection and recognition method was improved and designed to achieve rapid detection of shep-herd's purse seed germination,and detection experiments were conducted.Experimental results showed that YOLOv7-tiny achieved a germination discrimination accuracy of 92.7%for shepherd's purse seed,while improved DW-YOLOv7-tiny reached 96.2%.Compared to original data,parameter count and computational load were decreased by 3.32%and 4.35%,respectively.Model's accuracy,recall,and average precision mean increased by 3.5,2.5,and 1.8 percentage points,respectively.Model's weight file size was reduced by 1.0 MB,achieving greater lightweight performance while maintaining a detection speed of 121 f/s.These results demonstrate effective-ness of improved method,which is particularly suitable for rapid germination detection of ultra-small seeds like shepherd's purse in spe-cialized application scenarios.