MCE-ST: Classifying crop stress using hyperspectral data with a multiscale conformer encoder and spectral-based tokens

Sonobe, 2021, Use of spectral reflectance from a compact spectrometer to assess chlorophyll content in Zizania latifolia, Geocarto Int., 37, 5363, 10.1080/10106049.2021.1914747 Ang, 2021, Big data and machine learning with hyperspectral information in agriculture, IEEE Access, 9, 36699, 10.1109/ACCESS.2021.3051196 Lei, 2021, DOCC: Deep one-class crop classification via positive and unlabeled learning for multi-modal satellite imagery, Int. J. Appl. Earth Obs. Geoinf., 105 Mahlein, 2012, Recent advances in sensing plant diseases for precision crop protection, Eur. J. Plant Pathol., 133, 197, 10.1007/s10658-011-9878-z West, 2010, Detection of fungal diseases optically and pathogen inoculum by air sampling, 135 Blackburn, 2007, Hyperspectral remote sensing of plant pigments, J. Exp. Bot., 58, 855, 10.1093/jxb/erl123 Li, 2011, An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine, Knowl. Based Syst., 24, 40, 10.1016/j.knosys.2010.07.003 Moghimi, 2018, Ensemble feature selection for plant phenotyping: A journey from hyperspectral to multispectral imaging, IEEE Access, 6, 56870, 10.1109/ACCESS.2018.2872801 Khotimah, 2020, A high-performance spectral-spatial residual network for hyperspectral image classification with small training data, Remote Sens., 12, 3137, 10.3390/rs12193137 Zhong, 2018, Spectral–spatial residual network for hyperspectral image classification: A 3-D deep learning framework, IEEE Trans. Geosci. Remote Sens., 56, 847, 10.1109/TGRS.2017.2755542 Roy, 2019, Hybridsn: Exploring 3-d-2-d cnn feature hierarchy for hyperspectral image classification, IEEE Geosci. Remote Sens. Lett., 17, 277, 10.1109/LGRS.2019.2918719 Peng, Z., Huang, W., Gu, S., Xie, L., Wang, Y., Jiao, J., Ye, Q., 2021. Conformer: Local Features Coupling Global Representations for Visual Recognition. In: Proceeding IEEE/CVF Int. Conf. Comput. Vis.. pp. 367–376. Rao, 2022, Transferable network with Siamese architecture for anomaly detection in hyperspectral images, Int. J. Appl. Earth Obs. Geoinf., 106 Zhou, 2019, Hyperspectral image classification using spectral-spatial LSTMs, Neurocomputing, 328, 39, 10.1016/j.neucom.2018.02.105 Lea, C., Flynn, M.D., Vidal, R., Reiter, A., Hager, G.D., 2017. Temporal Convolutional Networks for Action Segmentation and Detection. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit.. pp. 156–165. Vaswani, 2017, Attention is all you need, Adv. Neural Inf. Process. Syst., 30, 5999 Khan, 2022, Transformers in vision: A survey, ACM Comput. Surv., 54, 1, 10.1145/3505244 Jamali, 2022, A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples, Int. J. Appl. Earth Obs. Geoinf., 115 Rao, 2022, Siamese Transformer Network for Hyperspectral Image Target Detection, IEEE Trans. Geosci. Remote Sens., 60, 10.1109/TGRS.2022.3163173 Hassani, 2021 Dosovitskiy, 2021 Hong, 2021, SpectralFormer: Rethinking hyperspectral image classification with transformers, IEEE Trans. Geosci. Remote Sens., 60, 1, 10.1109/TGRS.2022.3172371 He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit.. pp. 770–778. Raghu, 2021, Do vision transformers see like convolutional neural networks?, Adv. Neural Inf. Process. Syst., 34, 12116 He, 2020, HSI-BERT: Hyperspectral image classification using the bidirectional encoder representation from transformers, IEEE Trans. Geosci. Remote Sens., 58, 165, 10.1109/TGRS.2019.2934760 Mingote, 2023, Class Token and Knowledge Distillation for Multi-head Self-Attention Speaker Verification Systems, Digit. Signal Process., 133, 10.1016/j.dsp.2022.103859 Jiang, 2021, All tokens matter: Token labeling for training better vision transformers, Adv. Neural Inf. Process. Syst., 34, 18590 Liu, 2019 Chu, 2021 Gulati, 2020 Terentev, 2022, Current state of hyperspectral remote sensing for early plant disease detection: A review, Sensors, 22, 757, 10.3390/s22030757 Rapaport, 2015, Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-r) for grapevine water status assessment, ISPRS J. Photogramm. Remote Sens., 109, 88, 10.1016/j.isprsjprs.2015.09.003 Iliev, I., Krezhova, D., Yanev, T., Kirova, E., Alexieva, V., 2009. Response of chlorophyll fluorescence to salinity stress on the early growth stage of the soybean plants (Glycine max L.). In: 4th Int. Conf. Recent Adv. Sp. Technol.. ISBN: 9781424436286, pp. 403–407. http://dx.doi.org/10.1109/RAST.2009.5158234. Hernández, 2014, Spectral indices for the detection of salinity effects in melon plants, Sci. Agric., 71, 324, 10.1590/0103-9016-2013-0338 Raji, 2016, Detection and Classification of Mosaic Virus Disease in Cassava Plants by Proximal Sensing of Photochemical Reflectance Index, J. Indian Soc. Remote Sens., 44, 875, 10.1007/s12524-016-0565-6 Raji, 2015, Detection of mosaic virus disease in cassava plants by sunlight-induced fluorescence imaging: a pilot study for proximal sensing, Int. J. Remote Sens., 36, 2880, 10.1080/01431161.2015.1049382 Owomugisha, 2020, Early detection of plant diseases using spectral data, 1 Hamzeh, 2016, Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields, Int. J. Appl. Earth Obs. Geoinf., 52, 412 Dao, 2021, Plant drought impact detection using ultra-high spatial resolution hyperspectral images and machine learning, Int. J. Appl. Earth Obs. Geoinf., 102 Owomugisha, 2021, Matrix Relevance Learning from Spectral Data for Diagnosing Cassava Diseases, IEEE Access, 9, 83355, 10.1109/ACCESS.2021.3087231 Tu, 2022, A 1D-SP-net to determine early drought stress status of tomato (solanum lycopersicum) with imbalanced vis/NIR spectroscopy data, Agriculture, 12, 259, 10.3390/agriculture12020259 Hua, 2019, Dilated fully convolutional neural network for depth estimation from a single image, 612 Khotimah, 2022, SC-CAN: Spectral convolution and channel attention network for wheat stress classification, Remote Sens., 14, 4288, 10.3390/rs14174288 Wang, 2018, Understanding Convolution for Semantic Segmentation, 2018-Janua, 1451 Wang, 2022 Ramachandran, 2017 Moghimi, 2018, A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging, Front. Plant Sci., 9, 1182, 10.3389/fpls.2018.01182 Jin, 2018, Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field, Remote Sens., 10, 395, 10.3390/rs10030395 Mou, 2017, Deep recurrent neural networks for hyperspectral image classification, IEEE Trans. Geosci. Remote Sens., 55, 3639, 10.1109/TGRS.2016.2636241 Xu, 2018, Spectral-Spatial Unified Networks for Hyperspectral Image Classification, IEEE Trans. Geosci. Remote Sens., 56, 5893 Junaid, 2022, A comparative analysis of transformer based models for figurative language classification, Comput. Electr. Eng., 101, 10.1016/j.compeleceng.2022.108051