An improved YOLOv5s-based method for rapidly detecting strawberry ripeness was proposed to solve the problem of rapid and accurate identification of strawberry fruits in greenhouse. The Shuffle_Block was introduced as a feature extraction network in the backbone to lightweight the model.Meanwhile，the omni-dimensional dynamic convolution （ODConv） module was used in the neck structure to enhance the information mining ability of model for strawberry targets，reduce computational complexity，and further achieve lightweight.The results showed that the average precision of the improved YOLO-ODM model reached 97.4%.The model size is 7.79 Mb.The average detection time on the GPU is only 11 ms per image，and the floating-point operations are 6.9×10⁹ FLOPs.Compared with the original network，the lightweighted YOLO-ODM method improved the accuracy of detection while reducing model size by 43% and floating-point operations by 52%.It is indicated that the lightweighted method can rapidly and accurately detect the ripeness of strawberry fruit in greenhouse，monitor the growth status of strawberries.