An ensemble learning integration of multiple <scp>CNN</scp> with improved vision transformer models for pest classification-Reference-Cited by-同舟云学术

An ensemble learning integration of multiple CNN with improved vision transformer models for pest classification

Published:2022-10-05 Issue:2 Volume:182 Page:144-158
ISSN:0003-4746
Container-title:Annals of Applied Biology
language:en
Short-container-title:Annals of Applied Biology

Author:

Xia Wanshang¹^ORCID,Han Dezhi¹,Li Dun¹²,Wu Zhongdai³⁴,Han Bing³,Wang Junxiang⁴

Affiliation:

1. The College of Information Engineering Shanghai Maritime University Shanghai China

2. Telecom SudParis, IMT Institut Polytechnique de Paris Paris France

3. Shanghai Ship and Shipping Research Institute Co., Ltd. Shanghai China

4. COSCO Shipping Technology Co., Ltd. Shanghai China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Publisher

Wiley

Subject

Agronomy and Crop Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/aab.12804

Reference42 articles.

1. Alfarisy A. A. Chen Q. &Guo M.(2018).Deep learning based classification for paddy pests & diseases recognition. ICMAI'18: Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence.21–25.

2. Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks

3. SURF: Speeded Up Robust Features

4. Boureau Y. L. Ponce J. &Lecun Y.(2010).A theoretical analysis of feature pooling in visual recognition. ICML'10: Proceedings of the 27th International Conference on International Conference on Machine Learning.111–118.

5. Pest identification via deep residual learning in complex background

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