A plant image recognition method, which is based on effective region screening through a convolutional neural network (CNN), is proposed with an aim to improve the accuracy of plant image recognition in complex backgrounds. First, image (flower, leaf) datasets are used to train an effective region-screening model through a CNN, which is designed to allow the datasets to retain effective areas such as flowers and leaves after screening through the model. Subsequently, the effective areas are extracted from the plant image data sets by Mask R-CNN. Then the effective area screening model is used to screen the effective areas that can represent the plant image categories. The effective areas are divided into training sets and test sets in a ratio of 4∶1. The CNN plant image recognition model based on effective region selection (MRC-GoogleNet) is obtained after training in GoogleNet. Finally, the recognition accuracy is obtained through the model. The experimental results and data reveal that the recognition model, which is based on effective region selection, can more effectively extract image features and improve the recognition accuracy compared with the classical CNN plant image recognition model.