CNN and transformer framework for insect pest classification

Ecological Informatics - Tập 72 - Trang 101846 - 2022
Yingshu Peng1,2, Yi Wang3
1Lushan Botanical Garden, Chinese Academy of Sciences, Jiangxi Province 332900, PR China
2College of Forestry, Nanjing Forestry University, Nanjing 210037, PR China
3Jiangsu Wiscom Technology Co. Ltd, Nanjing 211100, PR China

Tài liệu tham khảo

Amarathunga, 2021, Methods of insect image capture and classification: a systematic literature review, Smart Agricult. Technol., 1, 10.1016/j.atech.2021.100023 Ayan, 2020, Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks, Comput. Electron. Agric., 179, 10.1016/j.compag.2020.105809 Bhojanapalli, 2021, Understanding robustness of transformers for image classification, 10231 Choudhary, 2020, A comprehensive survey on model compression and acceleration, Artif. Intell. Rev., 53, 5113, 10.1007/s10462-020-09816-7 Dawei, 2019, Recognition pest by image-based transfer learning, J. Sci. Food Agric., 99, 4524, 10.1002/jsfa.9689 Dong, 2020, A survey on ensemble learning, Front. Comput. Sci., 14, 241, 10.1007/s11704-019-8208-z Dosovitskiy, 2021, An image is worth 16x16 words: transformers for image recognition at scale, arXiv:2010.11929 Feng, 2022, MS-ALN: multiscale attention learning network for Pest recognition, IEEE Access, 10, 40888, 10.1109/ACCESS.2022.3167397 Fowler, 2021, The automatic classification of Pyriproxyfen-affected mosquito ovaries, Insects, 12, 1134, 10.3390/insects12121134 Han, 2022, A survey on vision transformer, IEEE Trans. Pattern Anal. Mach. Intell., 1–1 He, 2022, Transformers in medical image analysis: a review, arXiv:2202.12165 Heo, 2021, Rethinking spatial dimensions of vision transformers, 11936 Khan, 2021, Transformers in vision: a survey, arXiv:2101.01169 Kingsolver, 2011, Complex life cycles and the responses of insects to climate change, Integr. Comp. Biol., 51, 719, 10.1093/icb/icr015 Kolesnikov, 2020, Big transfer (BiT): General visual representation learning, 491 Larijani, 2019, Evaluation of image processing technique in identifying rice blast disease in field conditions based on KNN algorithm improvement by K-means, Food Sci. Nutr., 7, 3922, 10.1002/fsn3.1251 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Li, 2022, Image classification of pests with residual neural network based on transfer learning, Appl. Sci., 12, 4356, 10.3390/app12094356 Li, 2020, Crop pest recognition in natural scenes using convolutional neural networks, Comput. Electron. Agric., 169, 10.1016/j.compag.2019.105174 Liu, 2021, Plant diseases and pests detection based on deep learning: a review, Plant Methods, 17, 22, 10.1186/s13007-021-00722-9 Liu, 2020, DFF-ResNet: an insect pest recognition model based on residual networks, Big Data Min. Anal., 3, 300, 10.26599/BDMA.2020.9020021 Liu, 2021, Plant disease recognition: a large-scale benchmark dataset and a visual region and loss reweighting approach, IEEE Trans. Image Process., 30, 2003, 10.1109/TIP.2021.3049334 MacNeil, 2021, Plankton classification with high-throughput submersible holographic microscopy and transfer learning, BMC Ecol. Evol., 21, 123, 10.1186/s12862-021-01839-0 Naik, 2022, Detection and classification of chilli leaf disease using a squeeze-and-excitation-based CNN model, Ecol. Inform., 69, 10.1016/j.ecoinf.2022.101663 Nanni, 2022, High performing ensemble of convolutional neural networks for insect pest image detection, Ecol. Inform., 67, 10.1016/j.ecoinf.2021.101515 Pataki, 2021, Deep learning identification for citizen science surveillance of tiger mosquitoes, Sci. Rep., 11, 4718, 10.1038/s41598-021-83657-4 Perez, 2020, Audio-visual model distillation using acoustic images, 2843 Ramkumar, 2021, Cercospora identification in spinach leaves through Resnet-50 based image processing, J. Phys. Conf. Ser., 1717, 10.1088/1742-6596/1717/1/012046 Ren, 2019, Feature reuse residual networks for insect pest recognition, IEEE Access, 7, 122758, 10.1109/ACCESS.2019.2938194 Ridnik, 2021, ImageNet-21K Pretraining for the Masses, arXiv:2104.10972 Ridnik, 2021, ML-decoder: scalable and versatile classification head, arXiv:2111.12933 Roosjen, 2020, Deep learning for automated detection of Drosophila suzukii: potential for UAV-based monitoring, Pest Manag. Sci., 76, 2994, 10.1002/ps.5845 Takahashi, 2022, Confidence interval for micro-averaged F1 and macro-averaged F1 scores, Appl. Intell., 52, 4961, 10.1007/s10489-021-02635-5 Thenmozhi, 2019, Crop pest classification based on deep convolutional neural network and transfer learning, Comput. Electron. Agric., 164, 10.1016/j.compag.2019.104906 Ung, 2021, An efficient insect pest classification using multiple convolutional neural network based models, arXiv:2107.12189 Vabø, 2021, Automatic interpretation of salmon scales using deep learning, Ecol. Inform., 63, 10.1016/j.ecoinf.2021.101322 Wang, 2021, Convolutional neural network based automatic pest monitoring system using hand-held mobile image analysis towards non-site-specific wild environment, Comput. Electron. Agric., 187, 10.1016/j.compag.2021.106268 Wen, 2009, Local feature-based identification and classification for orchard insects, Biosyst. Eng., 104, 299, 10.1016/j.biosystemseng.2009.07.002 Wightman, 2021 Wu, 2021, CvT: Introducing convolutions to vision transformers, 22 Wu, 2019, IP102: A large-scale benchmark dataset for insect pest recognition, 8779 Xie, 2018, Multi-level learning features for automatic classification of field crop pests, Comput. Electron. Agric., 152, 233, 10.1016/j.compag.2018.07.014 Yang, 2020, A rapid rice blast detection and identification method based on crop disease spores’ diffraction fingerprint texture, J. Sci. Food Agric., 100, 3608, 10.1002/jsfa.10383 Yang, 2021, Recognizing pests in field-based images by combining spatial and channel attention mechanism, IEEE Access, 9, 162448, 10.1109/ACCESS.2021.3132486 Yao, 2014, Automated counting of Rice Planthoppers in Paddy fields based on image processing, J. Integr. Agric., 13, 1736, 10.1016/S2095-3119(14)60799-1 Yu, 2021, MetaFormer is actually what you need for vision, arXiv:2111.11418 Yuan, 2021, Incorporating convolution designs into visual transformers, 559