Abstract: The image recognition of small crops is very challenging for several reasons.First,the crop is small in size and a single sample is not representative of a collection.Second,different categories or different grades of the same crop may look very similar in shapes and colors.At present,there is a lack of research on image classification methods for small crops such as dried tea,rice and soybean,and most of the research datasets are taken in the laboratory environment with professional equipment,which brings difficulties to the practical application.For this,a method for image acquisition and processing of small crop samples using mobile phones is proposed.By taking tea and rice as a case study,we design a hierarchical network structure combined with two attention mechanisms.Through the coarse-grained to fine-grained classification process,coarse-grained classification is made first,namely different categories of samples,and then combined with two attention mechanisms,the network pays more attention to the diffe-rences between similar samples of different grades under the same category,so that they can be more accurate to classification of samples.Finally,the proposed method achieves the accuracy of 93.9% on the collected datasets.

Key words: Attention mechanism, Convolutional neural network, Hierarchical network structure, Image classification, Small crops