Abstract: To solve problems of background interference and information loss of ripe strawberries under natural state, a method for strawberry recognition was proposed based on deep residual learning in this study. Firstly, the depth-wise separable convolution was introduced to reduce the residual network parameters, the features of ripe strawberries were extracted from different aspects, and the strawberries in the classification layer were identified through the cross-entropy loss function. Additionally, the feature weights were learned by embedding compression and excitation modules, and feature recalibration was used to improve the learning and representation properties of the network. Finally, to further optimize the recognition results, the generalization ability of the model was improved by adding spatial pyramid pooling and weight decay optimization, which optimizes the recognition results. The experimental results demonstrate that compared with other current depth models, this method can effectively locate ripe strawberries under complex background and is not easily affected by the interference environment. With higher recognition accuracy and sensitivity, in data set C, the recognition accuracy and sensitivity are the highest, reaching 92.46% and 94.28%, respectively.