An ensemble learning integration of multiple CNN with improved vision transformer models for pest classification. (5th October 2022)

• Record Type: Journal Article
• Authors: Xia, Wanshang; Han, Dezhi; Li, Dun; Wu, Zhongdai; Han, Bing; Wang, Junxiang
• Is Part Of: Annals of applied biology. Volume 182:Number 2(2023)
• Page Start: 144
• Page End: 158
• Publication Date: 2022-10-05
• Subjects: convolutional neural networks -- ensemble learning -- insect classification -- vision transformer; Crop science -- Periodicals; Plants, Protection of -- Periodicals; Crops -- Ecology -- Periodicals; 630
• DOI: 10.1111/aab.12804
• Languages: English
• ISSNs: 0003-4746

Abstract

Abstract: Pests are the main threats to crop growth, and the precision classification of pests is conducive to formulating effective prevention and governance strategies. In response to the problems of low efficiency and inadaptability to the large‐scale environment of existing pest classification methods, this paper proposes a new pest classification method based on a convolutional neural network (CNN) and an improved Vision Transformer model. First, the MMAlNet is designed to extract the characteristics of the identification object from different scales and finer granularity. Then, a classification model called DenseNet Vision Transformer (DNVT) combining a CNN and an improved vision transformer model is proposed. The proposed DNVT captures both long distance dependencies and local characteristic modelling capabilities, which can effectively improve pest classification accuracy. Finally, the ensemble learning algorithm is used to learn MMAlNet and DNVT classification forecasts for soft voting, further enhancing the classification accuracy of pests. The simulation experiment results on the D0 and IP102 datasets show that the proposed method attained a maximum classification of 99.89 and 74.20%, respectively, which is better than other state‐of‐the‐art methods and has a high practical application value. Abstract : We propose CNN‐based DNVT and MMALNet models, followed by a soft voting method based on integrated learning.The results show that our method outperforms current state‐of‐the‐art methods, and has great potential for agricultural pest identification.