A target detection algorithm based EfficientDet-D1 model was proposed to meet the requirements of speed and accuracy in the rapid detection and classification of strawberries using the fast normalized feature weighted fusion feature in EfficientNet network to quickly identify the ripe and unripe strawberries in natural environments.The YOLOv3,YOLOv4,Faster-RCNN and EfficientDet-D0 models were used for comparative experiments.The results showed that mean average precision (mAP) of the five algorithms including YOLOv3,YOLOv4,Faster-RCNN,EfficientDet-D0 and EfficientDet-D1 was 89.51%,69.02%,96.54%,96.71%,and 97.50%,respectively.The detection performance of EfficientDet-D1 in the ripe and unripe strawberries is better than that of the other four target detection algorithms，which has better generalization and robustness,By using the EfficientNet network with a small number of model parameters.It is more suitable for mobile identification,and can provide a new solution for the automatic picking technology of the ripe strawberries.