Syed-Ab-Rahman, S. F., Hesamian, M. H. & Prasad, M. Citrus disease detection and classification using end-to-end anchor-based deep learning model. Appl. Intell. 52, 927–938 (2022).
Google Scholar
Argüeso, D. et al. Few-shot learning approach for plant disease classification using images taken in the field. Comput. Electron. Agric. 175, 105542 (2020).
Google Scholar
Feng, Q. et al. Online recognition of peanut leaf diseases based on the data balance algorithm and deep transfer learning. Precision Agric. 24, 560–586 (2023).
Google Scholar
Khamparia, A. et al. Seasonal crops disease prediction and classification using deep convolutional encoder network. Circuits Syst. Signal Process. 39, 818–836 (2019).
Google Scholar
Bhakta, I., Phadikar, S., Majumder, K., Mukherjee, H. & Sau, A. A novel plant disease prediction model based on thermal images using modified deep convolutional neural network. Precision Agric. 24, 23–39 (2023).
Google Scholar
Ferentinos, K. P. Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018).
Google Scholar
Rumpf, T. et al. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput. Electron. Agric. 74, 91–99 (2010).
Google Scholar
Munisami, T., Ramsurn, M., Kishnah, S. & Pudaruth, S. Plant leaf recognition using shape features and colour histogram with k-nearest neighbour classifiers. International Symposium on Computer Vision and the Internet India, pp. 740–741 (2015).
Qin, F. et al. Identification of alfalfa leaf diseases using image recognition technology. Plos ONE. 11, e0168274 (2016).
Google Scholar
Li, J. et al. MBNet: A multi-branch network for detecting the appearance of Korla pears. Comput. Electron. Agric. 206, 107660 (2023).
Google Scholar
Ding, J. Q., Li, B., Xu, C., Qiao, Y. & Zhang, L. X. Diagnosing crop diseases based on domain-adaptive pre-training BERT of electronic medical records. Appl. Intell. 53, 15979–15992 (2023).
Google Scholar
Wei, D., Chen, J., Luo, T., Long, T. & Wang, H. Classification of crop pests based on multi-scale feature fusion. Comput. Electron. Agric. 194, 106736 (2022).
Google Scholar
Johannes, A. et al. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Comput. Electron. Agric. 138, 200–209 (2017).
Google Scholar
Wang, D., Wang, J., Li, W. & Guan, P. T-CNN: Trilinear convolutional neural networks model for visual detection of plant diseases. Comput. Electron. Agric. 190, 106468 (2021).
Google Scholar
Yang, B. et al. From trained to untrained: A novel change detection framework using randomly initialized models with spatial–channel augmentation for hyperspectral images. IEEE Trans. Geosci. Remote Sens. 61, 1–14 (2023).
Google Scholar
Yang, B., Qin, L., Liu, J. & Liu, X. UTRNet: An unsupervised time-distance-guided convolutional recurrent network for change detection in irregularly collected images. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2022).
Google Scholar
Thai, H.-T., Le, K.-H. & Nguyen, N.L.-T. FormerLeaf: An efficient vision transformer for cassava leaf disease detection. Comput. Electron. Agric. 204, 107518 (2023).
Google Scholar
Wang, B. et al. An ultra-lightweight efficient network for image-based plant disease and pest infection detection. Precision Agric. (2023).
Zeng, T., Li, S., Song, Q., Zhong, F. & Wei, X. Lightweight tomato real-time detection method based on improved YOLO and mobile deployment. Comput. Electron. Agric. 205 (2023).
Zhao, X., Li, K., Li, Y., Ma, J. & Zhang, L. Identification method of vegetable diseases based on transfer learning and attention mechanism. Comput. Electron. Agric. 193, 106763 (2022).
Google Scholar
Gao, R., Wang, R., Feng, L., Li, Q. & Wu, H. Dual-branch, efficient, channel attention-based crop disease identification. Comput. Electron. Agric. 190 (2021).
Yang, M. J. et al. The research on detection of crop diseases ranking based on transfer learning. International Conference on Information Science and Control Engineering. 620–624 (2019).
Chen, W. et al. MS-DNet: A mobile neural network for plant disease identification. Comput. Electron. Agric. 199, 107175 (2022).
Google Scholar
Jiang, Z., Dong, Z., Jiang, W. & Yang, Y. Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning. Comput. Electron. Agric. 186 (2021).
Yang, B. et al. Identifying plant disease and severity from leaves: A deep multitask learning framework using triple-branch swin transformer and deep supervision. Comput. Electron. Agric. 209, 107809 (2023).
Google Scholar
Liang, Q. et al. PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network. Comput. Electron. Agric. 157, 518–529 (2019).
Google Scholar
Keceli, A. S., Kaya, A., Catal, C. & Tekinerdogan, B. Deep learning-based multi-task prediction system for plant disease and species detection. Ecol. Inform. 69 (2022).
Yu, B. & Lane, I. Multi-task deep learning for image understanding. International Conference of Soft Computing and Pattern Recognition, pp. 37–42 (2014).
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (2015).
Dosovitskiy, A. et al. An image is worth 16 × 16 words: Transformers for image recognition at scale. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition. (2021).
Liu, J., Liu, Z., Wang, L., Guo, L. & Dang, J. Speech emotion recognition with local-global aware deep representation learning. IEEE International Conference on Acoustics, Speech, and Signal Processing. 7174–7178 (2020).
She, X. & Zhang, D. Text classification based on hybrid CNN-LSTM hybrid model. International Symposium on Computational Intelligence and Design. 185–189 (2018).
Song, L., Liu, G. & Ma, M. TD-Net:unsupervised medical image registration network based on transformer and CNN. Appl. Intell. 52, 18201–18209 (2022).
Google Scholar
Lv, P., Wu, W., Zhong, Y., Du, F. & Zhang, L. SCViT: A spatial-channel feature preserving vision transformer for remote sensing image scene classification. IEEE Trans. Geosci. Remote Sens. 60, 1 (2022).
d’Ascoli, S. et al. ConViT: Improving vision transformers with soft convolutional inductive biases. J. Stat. Mech.-Theory Exp. 2022, 114005 (2022).
Google Scholar
Zhang, J., Meng, Z., Zhao, F., Liu, H. & Chang, Z. Convolution transformer mixer for hyperspectral image classification. IEEE Geosci. Remote Sensing Lett. 19, 1 (2022).
Gu, X. et al. Adaptive enhanced swin transformer with U-net for remote sensing image segmentation. Comput. Electr. Eng. 102 (2022).
Sun, L., Zhao, G., Zheng, Y. & Wu, Z. SpectralSpatial feature tokenization transformer for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 60, 1 (2022).
Google Scholar
Xu, S., An, X., Qiao, X. & Zhu, L. Multi-task least-squares support vector machines. Multim. Tools Appl. 71, 699–715 (2014).
Google Scholar
Wang, C. et al. A transformer-based method of multienergy load forecasting in integrated energy system. IEEE Trans. Smart Grid. 13, 2703–2714 (2022).
Google Scholar
Jiao, Z., Pan, L., Fan, W., Xu, Z. & Chen, C. Partly interpretable transformer through binary arborescent filter for intelligent bearing fault diagnosis. Measurement. 203 (2022).
Xiang, S. & Liang, Q. Remote sensing image compression with long-range convolution and improved non-local attention model. Signal Process. 209, 109005 (2023).
Google Scholar
Zhu, W. et al. Identifying field crop diseases using transformer-embedded convolutional neural network. Agric.-Basel. 12, 1083 (2022).
Vaswani, A. et al. Attention is all you need. Conference and Workshop on Neural Information Processing Systems. 30 (2017).
Li, X., Chen, X., Yang, J. & Li, S. Transformer helps identify kiwifruit diseases in complex natural environments. Comput. Electron. Agric. 200, 107258 (2022).
Google Scholar
Dong, Y. & Cordonnier, J.-B. Attention is not all you need: Pure attention loses rank doubly exponentially with depth. Int. Conf. Mach. Learn. (2021).
He, J., Zhao, L., Yang, H., Zhang, M. & Li, W. HSI-BERT: Hyperspectral image classification using the bidirectional encoder representation from transformers. IEEE Trans. Geosci. Remote Sens. 58, 165–178 (2020).
Google Scholar
Li, Q., Chen, Y. & Zeng, Y. Transformer with transfer CNN for remote-sensing-image object detection. Remote Sensing. 14, 984 (2022).
Google Scholar
Ghosh, P., Roy, S. K., Koirala, B., Rasti, B. & Scheunders, P. Hyperspectral unmixing using transformer network. IEEE Trans. Geosci. Remote Sens. 60 (2022).
Yang, L. et al. GoogLeNet based on residual network and attention mechanism identification of rice leaf diseases. Comput. Electron. Agric. 204, 107543 (2023).
Google Scholar
Yu, J., Li, J., Yu, Z. & Huang, Q. Multimodal transformer with multi-view visual representation for image captioning. IEEE Trans. Circuits Syst. Video Technol. 30, 4467–4480 (2020).
Google Scholar
Sun, Q., Chai, X., Zeng, Z., Zhou, G. & Sun, T. Multi-level feature fusion for fruit bearing branch keypoint detection. Comput. Electron. Agric. 191, 106479 (2021).
Google Scholar
Wang, F., Wang, R., Xie, C., Yang, P. & Liu, L. Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition. Comput. Electron. Agric. 169, 105222 (2020).
Google Scholar
Liu, S., Zhang, L. & Yang, X. Query2Label: A simple transformer way to multi-label classification. IEEE Conference on Computer Vision and Pattern Recognition. (2021).
Xiao, F., Guan, J., Lan, H., Zhu, Q. & Wang, W. Local information assisted attention-free decoder for audio captioning. IEEE Signal Process. Lett. 29, 1604–1608 (2022).
Google Scholar
Huang, Y. et al. Few-shot learning based on Attn-CutMix and task-adaptive transformer for the recognition of cotton growth state. Comput. Electr. Agric. 202 (2022).
He, K. M., Zhang, X. Y., Ren, S. Q. & Sun, J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition. 770–778 (2016).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).
Google Scholar
Wang, D., Wang, J., Ren, Z. & Li, W. DHBP: A dual-stream hierarchical bilinear pooling model for plant disease multi-task classification. Comput. Electron. Agric. 195, 106788 (2022).
Google Scholar
Wang, F. et al. Practical cucumber leaf disease recognition using improved swin transformer and small sample size. Comput. Electron. Agric. 199, 107163 (2022).
Google Scholar
Antolínez García, A. & Cáceres Campana, J. W. Identification of pathogens in corn using near-infrared UAV imagery and deep learning. Precision Agric. 24, 783–806 (2023).
Google Scholar
Selvaraju, R. R. et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. IEEE Int. J. Comput. Vis. 618–626 (2017).
Zeng, W. & Li, M. Crop leaf disease recognition based on self-attention convolutional neural network. Comput. Electron. Agric. 172 (2020).
