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Tomato leaf-type diseases have the characteristics of large intra-class differences and small inter-class differences in the early and late stages. The conventional neural network is not effective in classifying such diseases. Therefore, based on the fine-grained weakly supervised classification method, a Res2Net bilinear attention network, combining the bilinear model and attention mechanism, was proposed. The fine-grained representation ability was improved through extracting multi-scale features and combining the attention mechanism. First of all, for the problem of information loss in the process of conventional channel attention acquisition, the EFCA channel attention module was proposed. On the basis of no dimensionality reduction, two-dimensional discrete cosine transform was used instead of global average pooling to avoid some features from being lost in downsampling. Secondly, by adding the maximum pooling after the outer product, and the concat module designed by drawing on the shortcut idea in the residual network, the problem of redundant features caused by the excessively high dimensionality after bilinear fusion was solved. The obtained classification accuracies of the proposed model on the data set with 7 types and 14 different degrees of tomato leaf type diseases were 98.66% and 86.89%, respectively.