孙佳照, 冉渝澳, 冯俊, 等. 西南地区烟草潜在适生区预测[J]. 中国烟草科学, 2023, 44(5): 37-44, 61.
WANG G L, ZHU Q K, SONG C D, et al. MedKAFormer: When Kolmogorov-Arnold Theorem Meets Vision Transformer for Medical Image Representation[J]. IEEE Journal of Biomedical and Health Informatics, 2025, 29(6): 4303-4313. doi: 10.1109/JBHI.2025.3541982
WU Y H, LIU Y, ZHAN X, et al. P2T: Pyramid Pooling Transformer for Scene Understanding[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(11): 12760-12771. doi: 10.1109/TPAMI.2022.3202765
HARIDASAN A, THOMAS J, RAJ E D. Deep Learning System for Paddy Plant Disease Detection and Classification[J]. Environmental Monitoring and Assessment, 2023, 195(1): 120. doi: 10.1007/s10661-022-10656-x
孙佳照, 李群岭, 林小兴, 等. 基于Resnet-101模型的烟蚜数量图像识别系统开发[J]. 植物医学, 2024, 3(4): 26-31. doi: 10.13718/j.cnki.zwyx.2024.04.004
LI Z C, ZHOU G X, HU Y W, et al. Maize Leaf Disease Identification Based on WG-MARNet[J]. PLoS One, 2022, 17(4): e0267650. doi: 10.1371/journal.pone.0267650
LIU H, CUI Y D, WANG J M, et al. Analysis and Research on Rice Disease Identification Method Based on Deep Learning[J]. Sustainability, 2023, 15(12): 9321. doi: 10.3390/su15129321
BEGUM N, HAZARIKA M K. Prediction of Physico-Chemical Properties in Tomatoes Using Deep Neural Architecture[J]. Agricultural Research, 2024: 1-11.
LIN J W, CHEN Y, PAN R Y, et al. CAMFFNet: a Novel Convolutional Neural Network Model for Tobacco Disease Image Recognition[J]. Computers and Electronics in Agriculture, 2022, 202: 107390. doi: 10.1016/j.compag.2022.107390
WU T N, ZHANG Y W, GONG Z W, et al. Quantification of Tobacco Leaf Appearance Quality Index Based on Computer Vision[J]. IEEE Access, 2022, 10: 120352-120368. doi: 10.1109/ACCESS.2022.3221978
HAN K, WANG Y H, CHEN H T, et al. A Survey on Vision Transformer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(1): 87-110. doi: 10.1109/TPAMI.2022.3152247
HE F Y, LIU Y, LIU J F. ECA-ViT: Leveraging ECA and Vision Transformer for Crop Leaves Diseases Identification in Cultivation Environments[C]//2024 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE). June 28-30, 2024. Zhuhai, China. IEEE, 2024: 101-104 DOI: 10.1109/mlise62164.2024.10674238.
SCHMIDT-HIEBER J. The Kolmogorov-Arnold Representation Theorem Revisited[J]. Neural Networks, 2021, 137: 119-126. doi: 10.1016/j.neunet.2021.01.020
SILVEIRA A C C, DO CARMO D S, UEDA L H, et al. VITMST++: Efficient Hyperspectral Reconstruction through Vision Transformer-Based Spatial Compression[J]. IEEE Open Journal of Signal Processing, 2025, 6: 398-404. doi: 10.1109/OJSP.2025.3544891
ZENG Z H, LIU C B, TANG Z, et al. AccTFM: an Effective Intra-Layer Model Parallelization Strategy for Training Large-Scale Transformer-Based Models[J]. IEEE Transactions on Parallel and Distributed Systems, 2022, 33(12): 4326-4338. doi: 10.1109/TPDS.2022.3187815
MACDOWALL F D H. Predisposition of Tobacco to Ozone Damage[J]. Canadian Journal of Plant Science, 1965, 45(1): 1-12. doi: 10.4141/cjps65-001
XUE W X, XU P J, WANG X F, et al. Natural-Enemy-Based Biocontrol of Tobacco Arthropod Pests in China[J]. Agronomy, 2023, 13(8): 1972. doi: 10.3390/agronomy13081972
HAQUE M A, DEB C K, GOLE P, et al. An Enhanced Vision Transformer Network for Efficient and Accurate Crop Disease Detection[J]. Expert Systems with Applications, 2025, 283: 127743. doi: 10.1016/j.eswa.2025.127743
MONTAVON G, SAMEK W, MVLLER K R. Methods for Interpreting and Understanding Deep Neural Networks[J]. Digital Signal Processing, 2018, 73: 1-15. doi: 10.1016/j.dsp.2017.10.011
SHARMA S K, VISHWAKARMA D K. Classification of Banana Plant Leaves Based on Nutrient Deficiency Using Vision Transformer[C]//2024 5th International Conference for Emerging Technology (INCET). May 24-26, 2024, Belgaum, India. IEEE, 2024: 1-6.
冉渝澳, 金亚波, 王振国, 等. 烟草靶斑病预测模型构建及数字化应用研发[J]. 植物医学, 2024, 3(4): 40-49. doi: 10.13718/j.cnki.zwyx.2024.04.006
SHINODA R, KATAOKA H, HARA K, et al. Transformer-Based Ripeness Segmentation for Tomatoes[J]. Smart Agricultural Technology, 2023, 4: 100196. doi: 10.1016/j.atech.2023.100196