{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T09:50:16Z","timestamp":1781949016126,"version":"3.54.5"},"reference-count":55,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T00:00:00Z","timestamp":1661817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Finance","award":["UWA2002-003RTX"],"award-info":[{"award-number":["UWA2002-003RTX"]}]},{"DOI":"10.13039\/501100000980","name":"Grains Research and Development Corporation Grant","doi-asserted-by":"publisher","award":["UWA2002-003RTX"],"award-info":[{"award-number":["UWA2002-003RTX"]}],"id":[{"id":"10.13039\/501100000980","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Biotic and abiotic plant stress (e.g., frost, fungi, diseases) can significantly impact crop production. It is thus essential to detect such stress at an early stage before visual symptoms and damage become apparent. To this end, this paper proposes a novel deep learning method, called Spectral Convolution and Channel Attention Network (SC-CAN), which exploits the difference in spectral responses of healthy and stressed crops. The proposed SC-CAN method comprises two main modules: (i) a spectral convolution module, which consists of dilated causal convolutional layers stacked in a residual manner to capture the spectral features; (ii) a channel attention module, which consists of a global pooling layer and fully connected layers that compute inter-relationship between feature map channels before scaling them based on their importance level (attention score). Unlike standard convolution, which focuses on learning local features, the dilated convolution layers can learn both local and global features. These layers also have long receptive fields, making them suitable for capturing long dependency patterns in hyperspectral data. However, because not all feature maps produced by the dilated convolutional layers are important, we propose a channel attention module that weights the feature maps according to their importance level. We used SC-CAN to classify salt stress (i.e., abiotic stress) on four datasets (Chinese Spring (CS), Aegilops columnaris (co(CS)), Ae. speltoides auchery (sp(CS)), and Kharchia datasets) and Fusarium head blight disease (i.e., biotic stress) on Fusarium dataset. Reported experimental results show that the proposed method outperforms existing state-of-the-art techniques with an overall accuracy of 83.08%, 88.90%, 82.44%, 82.10%, and 82.78% on CS, co(CS), sp(CS), Kharchia, and Fusarium datasets, respectively.<\/jats:p>","DOI":"10.3390\/rs14174288","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:13:56Z","timestamp":1661904836000},"page":"4288","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["SC-CAN: Spectral Convolution and Channel Attention Network for Wheat Stress Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7541-3772","authenticated-orcid":false,"given":"Wijayanti Nurul","family":"Khotimah","sequence":"first","affiliation":[{"name":"Department of Computer Science and Software Engineering, The University of Western Australia, Perth, WA 6009, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farid","family":"Boussaid","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, Perth, WA 6009, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1557-4907","authenticated-orcid":false,"given":"Ferdous","family":"Sohel","sequence":"additional","affiliation":[{"name":"Information Technology, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6603-3257","authenticated-orcid":false,"given":"Lian","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, The University of Western Australia, Perth, WA 6009, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7599-6760","authenticated-orcid":false,"given":"David","family":"Edwards","sequence":"additional","affiliation":[{"name":"School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0827-2962","authenticated-orcid":false,"given":"Xiu","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Bennamoun","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, The University of Western Australia, Perth, WA 6009, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sarwat, M., Ahmad, A., Abdin, M.Z., and Ibrahim, M.M. (2016). Stress Signaling in Plants: Genomics and Proteomics Perspective, Springer International Publishing.","DOI":"10.1007\/978-3-319-42183-4"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1111\/nph.12797","article-title":"Abiotic and biotic stress combinations","volume":"203","author":"Suzuki","year":"2014","journal-title":"New Phytol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"114770","DOI":"10.1016\/j.eswa.2021.114770","article-title":"Rice diseases detection and classification using attention based neural network and bayesian optimization","volume":"178","author":"Wang","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5353","DOI":"10.1007\/s00521-020-05325-4","article-title":"Identifying crop water stress using deep learning models","volume":"33","author":"Chandel","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","article-title":"Plant Disease Detection by Imaging Sensors\u2013Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping","volume":"100","author":"Mahlein","year":"2016","journal-title":"Plant Dis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.knosys.2010.07.003","article-title":"An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine","volume":"24","author":"Li","year":"2011","journal-title":"Knowl.-Based Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","article-title":"Deep Learning for Classification of Hyperspectral Data: A Comparative Review","volume":"7","author":"Audebert","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/S1672-0229(08)60021-2","article-title":"Fuzzy Logic for Elimination of Redundant Information of Microarray Data","volume":"6","author":"Huerta","year":"2008","journal-title":"Genom. Proteom. Bioinform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.3389\/fpls.2018.01182","article-title":"A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging","volume":"9","author":"Moghimi","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"56870","DOI":"10.1109\/ACCESS.2018.2872801","article-title":"Ensemble Feature Selection for Plant Phenotyping: A Journey from Hyperspectral to Multispectral Imaging","volume":"6","author":"Moghimi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep Learning for Hyperspectral Image Classification: An Overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1016\/j.neucom.2015.10.004","article-title":"Iterative deep learning for image set based face and object recognition","volume":"174","author":"Shah","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.R., and Hinton, G. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceedings of the ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"113008","DOI":"10.1016\/j.eswa.2019.113008","article-title":"Predicting auction price of vehicle license plate with deep recurrent neural network","volume":"142","author":"Chow","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep recurrent neural networks for hyperspectral image classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2018.02.105","article-title":"Hyperspectral image classification using spectral-spatial LSTMs","volume":"328","author":"Zhou","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_17","unstructured":"Hochreiter, S., Bengio, Y., Frasconi, P., and Schmidhuber, J. (2001). A field guide to dynamical recurrent neural networks. Gradient Flow in Recurrent Nets: The Difficulty of Learning Long-Term Dependencies, Wiley-IEEE Press."},{"key":"ref_18","unstructured":"Lipton, Z.C., Berkowitz, J., and Elkan, C. (2015). A Critical Review of Recurrent Neural Networks for Sequence Learning. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhou, F., Hang, R., and Yuan, X. (2017). Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification. Remote Sens., 9.","DOI":"10.3390\/rs9121330"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lea, C., Flynn, M.D., Vidal, R., Reiter, A., and Hager, G.D. (2017, January 21\u201326). Temporal Convolutional Networks for Action Segmentation and Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.113"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/258619","article-title":"Deep Convolutional Neural Networks for Hyperspectral Image Classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Peng, Z., Huang, W., Gu, S., Xie, L., Wang, Y., Jiao, J., and Ye, Q. (2021, January 11\u201317). Conformer: Local Features Coupling Global Representations for Visual Recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00042"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jin, X., Jie, L., Wang, S., Qi, H., and Li, S. (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.","DOI":"10.3390\/rs10030395"},{"key":"ref_24","first-page":"2","article-title":"WaveNet: A generative model for raw audio","volume":"125","author":"Dieleman","year":"2016","journal-title":"SSW"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.neucom.2019.10.081","article-title":"DCGSA: A global self-attention network with dilated convolution for crowd density map generating","volume":"378","author":"Zhu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., and Chua, T.S. (2017, January 21\u201326). SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.667"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.neucom.2021.07.034","article-title":"Filter pruning with a feature map entropy importance criterion for convolution neural networks compressing","volume":"461","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.ijfoodmicro.2017.04.011","article-title":"Agricultural factors affecting Fusarium communities in wheat kernels","volume":"252","author":"Karlsson","year":"2017","journal-title":"Int. J. Food Microbiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1094\/CCHEM-11-16-0271-R","article-title":"Estimation of the Deoxynivalenol and Moisture Contents of Bulk Wheat Grain Samples by FT-NIR Spectroscopy","volume":"94","author":"Peiris","year":"2017","journal-title":"Cereal Chem. J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Iliev, I., Krezhova, D., Yanev, T., Kirova, E., and Alexieva, V. (2009, January 11\u201313). Response of chlorophyll fluorescence to salinity stress on the early growth stage of the soybean plants (Glycine max L.). Proceedings of the RAST 2009\u2014Proceedings of 4th International Conference on Recent \nAdvances Space Technologies, Istanbul, Turkey.","DOI":"10.1109\/RAST.2009.5158234"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1590\/0103-9016-2013-0338","article-title":"Spectral indices for the detection of salinity effects in melon plants","volume":"71","year":"2014","journal-title":"Sci. Agric."},{"key":"ref_33","first-page":"412","article-title":"Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields","volume":"52","author":"Hamzeh","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.neucom.2019.11.092","article-title":"Deep hybrid dilated residual networks for hyperspectral image classification","volume":"384","author":"Cao","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1109\/LGRS.2019.2960528","article-title":"DSSNet: A Simple Dilated Semantic Segmentation Network for Hyperspectral Imagery Classification","volume":"17","author":"Pan","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pooja, K., Nidamanuri, R.R., and Mishra, D. (2019, January 14\u201316). Multi-Scale Dilated Residual Convolutional Neural Network for Hyperspectral Image Classification. Proceedings of the Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS.2019.8921284"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hamaguchi, R., Fujita, A., Nemoto, K., Imaizumi, T., and Hikosaka, S. (2018, January 12\u201315). Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision WACV, Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00162"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s11676-021-01378-w","article-title":"Spectroscopic detection of forest diseases: A review (1970\u20132020)","volume":"33","author":"Cotrozzi","year":"2022","journal-title":"J. For. Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hou, J., Wang, G., Chen, X., Xue, J.H., Zhu, R., and Yang, H. (2018, January 8\u201314). Spatial-Temporal Attention Res-TCN for Skeleton-based Dynamic Hand Gesture Recognition. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11024-6_18"},{"key":"ref_40","unstructured":"Zagoruyko, S., and Komodakis, N. (2017, January 24\u201326). Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. Proceedings of the 5th International Conference on Learning Representations, ICLR 2017\u2014Conference Track Proceedings, Toulon, France."},{"key":"ref_41","first-page":"5999","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Cheng, J., Dong, L., and Lapata, M. (2016, January 1\u20135). Long Short-Term Memory-Networks for Machine Reading. Proceedings of the EMNLP 2016\u2014Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA.","DOI":"10.18653\/v1\/D16-1053"},{"key":"ref_43","unstructured":"Lin, Z., Feng, M., dos Santos, C.N., Yu, M., Xiang, B., Zhou, B., and Bengio, Y. (2017). A Structured Self-attentive Sentence Embedding. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Parikh, A.P., T\u00e4ckstr\u00f6m, O., Das, D., and Uszkoreit, J. (2016, January 1\u20135). A Decomposable Attention Model for Natural Language Inference. Proceedings of the EMNLP 2016\u2014Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA.","DOI":"10.18653\/v1\/D16-1244"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/TGRS.2019.2933609","article-title":"Learning to Pay Attention on Spectral Domain: A Spectral Attention Module-Based Convolutional Network for Hyperspectral Image Classification","volume":"58","author":"Mou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103340","DOI":"10.1016\/j.infrared.2020.103340","article-title":"Spectral group attention networks for hyperspectral image classification with spectral separability analysis","volume":"108","author":"Liu","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"42384","DOI":"10.1109\/ACCESS.2020.2977454","article-title":"Hyperspectral Band Selection Using Attention-Based Convolutional Neural Networks","volume":"8","author":"Tulczyjew","year":"2020","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_49","first-page":"5500413","article-title":"Feature-Grouped Network with Spectral-Spatial Connected Attention for Hyperspectral Image Classification","volume":"60","author":"Guo","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","unstructured":"Zhu, X., Cheng, D., Zhang, Z., Lin, S., and Dai, J. (November, January 27). An Empirical Study of Spatial Attention Mechanisms in Deep Networks. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Farha, Y.A., and Gall, J. (2019, January 15\u201320). MS-TCN: Multi-stage temporal convolutional network for action segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00369"},{"key":"ref_52","first-page":"4797","article-title":"Conditional Image Generation with PixelCNN Decoders","volume":"29","author":"Kalchbrenner","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Khotimah, W.N., Bennamoun, M., Boussaid, F., Sohel, F., and Edwards, D. (2020). A high-performance spectral-spatial residual network for hyperspectral image classification with small training data. Remote Sens., 12.","DOI":"10.3390\/rs12193137"},{"key":"ref_54","first-page":"5893","article-title":"Spectral-Spatial Unified Networks for Hyperspectral Image Classification","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","first-page":"5518615","article-title":"SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers","volume":"60","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4288\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:20:37Z","timestamp":1760142037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4288"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,30]]},"references-count":55,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174288"],"URL":"https:\/\/doi.org\/10.3390\/rs14174288","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,30]]}}}