| [1] | RISTAINO J B, ANDERSON P K, BEBBER D P, et al. The persistent threat of emerging plant disease pandemics to global food security[J/OL]. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(23): e2022239118[2025-03-28]. DOI: 10.1073/pnas.2022239118. |
| [2] | WU Xue, DENG Hongyu, WANG Qi, et al. Meta-learning shows great potential in plant disease recognition under few available samples [J]. The Plant Journal, 2023, 114(4): 767−782. |
| [3] | 郑倩. 基于文献计量学和机器学习的小麦生物育种文献分析[J]. 浙江农林大学学报, 2025, 42(1): 210−217. ZHENG Qian. Literature analysis of Triticum aestivum bio-breeding based on bibliometrics and machine learning [J]. Journal of Zhejiang A&F University, 2025, 42(1): 210−217. |
| [4] | MOHANTY S P, HUGHES D P, SALATHÉ M. Using deep learning for image-based plant disease detection[J/OL]. Frontiers in Plant Science, 2016, 7: 1419[2025-03-28]. DOI: 10.3389/fpls.2016.01419. |
| [5] | 明浩, 苏喜友. 利用特征分割和病斑增强的杨树叶部病害识别[J]. 浙江农林大学学报, 2020, 37(6): 1159−1166. MING Hao, SU Xiyou. Image recognition of poplar leaf diseases with feature segmentation and lesion enhancement [J]. Journal of Zhejiang A&F University, 2020, 37(6): 1159−1166. |
| [6] | 王冠, 王建新, 孙钰. 面向边缘计算的轻量级植物病害识别模型[J]. 浙江农林大学学报, 2020, 37(5): 978−985. WANG Guan, WANG Jianxin, SUN Yu. Lightweight plant disease recognition model for edge computing [J]. Journal of Zhejiang A&F University, 2020, 37(5): 978−985. |
| [7] | BRAHIMI M, BOUKHALFA K, MOUSSAOUI A. Deep learning for tomato diseases: classification and symptoms visualization [J]. Applied Artificial Intelligence, 2017, 31(4): 299−315. |
| [8] | FERENTINOS K P. Deep learning models for plant disease detection and diagnosis [J]. Computers and Electronics in Agriculture, 2018, 145: 311−318. |
| [9] | LI Xiaoxu, YANG Xiaochen, MA Zhanyu, et al. Deep metric learning for few-shot image classification: a review of recent developments[J/OL]. Pattern Recognition, 2023, 138: 109381[2025-03-28]. DOI: 10.1016/j.patcog.2023.109381. |
| [10] | JI Zhong, CHAI Xingliang, YU Yunlong, et al. Improved prototypical networks for few-shot learning [J]. Pattern Recognition Letters, 2020, 140: 81−87. |
| [11] | BERTINETTO L, HENRIQUES J F, TORR P H S, et al. Meta-learning with differentiable closed-form solvers[C]//Proceedings of the 7th International Conference on Learning Representations. San Diego: ICLR, 2019. |
| [12] | ZHAO Kangkang, ZHANG Ziyan, JIANG Bo, et al. LGLNN: label guided graph learning-neural network for few-shot learning [J]. Neural Networks, 2022, 155: 50−57. |
| [13] | HU Gensheng, WU Haoyu, ZHANG Yan, et al. A low shot learning method for tea leaf’s disease identification[J/OL]. Computers and Electronics in Agriculture, 2019, 163: 104852[2025-03-28]. DOI: 10.1016/j.compag.2019.104852. |
| [14] | LI Yang, YANG Jiachen. Few-shot cotton pest recognition and terminal realization[J/OL]. Computers and Electronics in Agriculture, 2020, 169: 105240[2025-03-28]. DOI: 10.1016/j.compag.2020.105240. |
| [15] | GARG S, SINGH P. An aggregated loss function based lightweight few shot model for plant leaf disease classification [J]. Multimedia Tools and Applications, 2023, 82(15): 23797−23815. |
| [16] | USKANER HEPSAĞ P. Efficient plant disease identification using few-shot learning: a transfer learning approach [J]. Multimedia Tools and Applications, 2024, 83(20): 58293−58308. |
| [17] | BOULILA W. An approach based on performer-attention-guided few-shot learning model for plant disease classification [J]. Earth Science Informatics, 2024, 17(4): 3797−3809. |
| [18] | THAKUR P S, KHANNA P, SHEOREY T, et al. Explainable Vision Transformer Enabled Convolutional Neural Network for Plant Disease Identification: PlantXViT[EB/OL]. 2022-07-16[2025-03-28]. https://doi.org/10.48550/arXiv.2207.07919. |
| [19] | WANG Yuzhi, YIN Yunzhen, LI Yaoyu, et al. Classification of plant leaf disease recognition based on self-supervised learning[J/OL]. Agronomy, 2024, 14(3): 500[2025-03-28]. DOI: 10.3390/agronomy14030500. |
| [20] | CHEN Xuanchi, ZHENG Xiangwei, SUN Kai, et al. Self-supervised vision transformer-based few-shot learning for facial expression recognition [J]. Information Sciences, 2023, 634: 206−226. |
| [21] | LI Yehao, YAO Ting, PAN Yingwei, et al. Contextual transformer networks for visual recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(2): 1489−1500. |
| [22] | 肖伟, 冯全, 张建华, 等. 基于小样本学习的植物病害识别研究[J]. 中国农机化学报, 2021, 42(11): 138−143. XIAO Wei, FENG Quan, ZHANG Jianhua, et al. Research on plant disease identification based on few-shot learning [J]. Journal of Chinese Agricultural Mechanization, 2021, 42(11): 138−143. |
| [23] | LI Yang, CHAO Xuewei. Semi-supervised few-shot learning approach for plant diseases recognition[J/OL]. Plant Methods, 2021, 17(1): 68[2025-03-28]. DOI: 10.1186/s13007-021-00770-1. |
| [24] | NI Jing, YUAN Yichen, LI Yang, et al. Few-shot learning in intelligent agriculture: a review of methods and applications [J]. Journal of Agricultural Sciences, 2024, 30(2): 216−228. |
| [25] | LIN Hong, TSE R, TANG S K, et al. Few-shot learning approach with multi-scale feature fusion and attention for plant disease recognition[J/OL]. Frontiers in Plant Science, 2022, 13: 907916[2025-03-28]. DOI: 10.3389/fpls.2022.907916. |
| [26] | LIN Hong, TSE R, TANG S K, et al. Few-shot learning for plant-disease recognition in the frequency domain[J/OL]. Plants, 2022, 11(21): 2814[2025-03-28]. DOI: 10.3390/plants11212814. |
| [27] | LIN Hong, QIANG Zhenping, TSE R, et al. A few-shot learning method for tobacco abnormality identification[J/OL]. Frontiers in Plant Science, 2024, 15: 1333236[2025-03-28]. DOI: 10.3389/fpls.2024.1333236. |
| [28] | YAN Wenbo, FENG Quan, YANG Sen, et al. Prune-FSL: pruning-based lightweight few-shot learning for plant disease identification[J/OL]. Agronomy, 2024, 14(9): 1878[2025-03-28]. DOI: 10.3390/agronomy14091878. |
| [29] | REZAEI M, DIEPEVEEN D, LAGA H, et al. Plant disease recognition in a low data scenario using few-shot learning[J/OL]. Computers and Electronics in Agriculture, 2024, 219: 108812[2025-03-28]. DOI: 10.1016/j.compag.2024.108812. |