Classification Method of Small Sample Apple Leaves Based on SE-ResNeXt

Abstract

Abstract: Based on the existing deep learning technology， this study adopts the variant SE-ResNeXt based on residual neural network to construct a convolutional neural network model wich can automatically classify apple varieties and train the model based on transfer learning method. The data is taken from 20 types apple leaves images taken at the Apple Industry Base in Jingning County， Gansu Province. There are 50 pictures of each type of apple leaves， 1000 pictures in total. On this dataset， six models， likes ResNet50，ResNet101，SE-ResNet50，SE-ResNet101，SE-ResNeXt50 and SE-ResNeXt101， are carried out comparison experiments. The results show that SE-ResNeXt101 outperforms other comparison models， with the highest accuracy rate of 97.5% and the inference time of single image only 0.125 s. The method proposed in this paper provides a mean for identifying apple varieties efficiently and accurately in the future， and can be a great help for assisting agricultural research and apple planting.

Key words: deep learning, residual neural network, transfer learning, apple classification, apple leaf image

BAI Xu-guang, LIU Cheng-zhong, HAN Jun-ying, GAO Jia-meng, CHEN Jun-kang. Classification Method of Small Sample Apple Leaves Based on SE-ResNeXt[J]. Computer and Modernization, 2023, 0(01): 18-23.

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