Fine-grained classification of grape leaves via a pyramid residual convolution neural network  

作　　者：Hanghao Li Yana Wei Hongming Zhang Huan Chen Jiangfei Meng 

机构地区：[1]College of Information Engineering,Northwest A&F University,Yangling 712100,Shaanxi,China [2]College of Enology,Northwest A&F University,Yangling 712100,Shaanxi,China

出　　处：《International Journal of Agricultural and Biological Engineering》2022年第2期197-203,共7页国际农业与生物工程学报（英文）

基　　金：This work was financially supported by the National Key Research and Development Project(Grant No.2020YFD1100601)。

摘　　要：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.

关 键 词：fine-grained classification grape cultivars identification pyramid residual network convolution neural network 

分 类 号：S51[农业科学—作物学]