The value of grape cultivars varies. The use of a mixture of cultivars can negate the benefits of improved cultivars and hamper the protection of genetic resources and the identification of new hybrid cultivars. Classifying cultivars based on their leaves is therefore highly practical. Transplanted grape seedlings take years to bear fruit, but leaves mature in months. Foliar morphology differs among cultivars, so identifying cultivars based on leaves is feasible. Different cultivars, however, can be bred from the same parents, so the leaves of some cultivars can have similar morphologies. In this work, a pyramid residual convolution neural network was developed to classify images of eleven grape cultivars. The model extracts multi-scale feature maps of the leaf images through the convolution layer and enters them into three residual convolution neural networks. Features are fused by adding the value of the convolution kernel feature matrix to enhance the attention on the edge and center regions of the leaves and classify the images. The results indicated that the average accuracy of the model was 92.26% for the proposed leaf dataset. The proposed model is superior to previous models and provides a reliable method for the fine-grained classification and identification of plant cultivars. Keywords: fine-grained classification, grape cultivars identification, pyramid residual network, convolution neural network DOI: 10.25165/j.ijabe.20221502.6894 Citation: Li H H, Wei Y N, Zhang H M, Chen H, Meng J F. Fine-grained classification of grape leaves via a pyramid residual convolution neural network. Int J Agric & Biol Eng, 2022; 15(2): 197–203.

The elimination of mixed errors is a key preprocessing technology for the area of digital elevation model data analysis, which is important for further applying data. We associated group sparsity with the low-rank uniqueness of local transformations of mixing errors to effectively remove mixing errors in data from Shuttle Radar Topography Mission 1 (SRTM 1) based on the sparseness of low-rank groups. First, the stripe-error structure that appeared globally in multiple directions was able to be better represented locally using group-sparse regularization and the uniqueness of the data in the low-rank direction of the local range and using variational ideas to constrain the gradient direction of the data to avoid redundant elimination. Second, the nonlocal self-similarity of the weighted kernel norm was used to remove random noise. Finally, the proposed model for eliminating mixed errors was solved using an algorithm based on the multiplier method of alternating direction. Experiments using simulated and real data found that the proposed low-rank group-sparse method (LRGS) eliminated mixed errors in both visual and quantitative evaluations better than the most recent processing methods and existing dataset products.