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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. [ABSTRACT FROM AUTHOR]

Abstract The science of learning has made a considerable contribution to our understanding of effective teaching and learning strategies. However, few instructors outside of the field are privy to this research. In this tutorial review, we focus on six specific cognitive strategies that have received robust support from decades of research: spaced practice, interleaving, retrieval practice, elaboration, concrete examples, and dual coding. We describe the basic research behind each strategy and relevant applied research, present examples of existing and suggested implementation, and make recommendations for further research that would broaden the reach of these strategies.