{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T16:03:16Z","timestamp":1773849796732,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFE0194900"],"award-info":[{"award-number":["2017YFE0194900"]}]},{"name":"National Key R&amp;D Program of China","award":["130"],"award-info":[{"award-number":["130"]}]},{"name":"2019 Hunan Postgraduate High-Quality Course Project \u201cMicrowave and Hyperspectral Remote Sensing\u201d","award":["2017YFE0194900"],"award-info":[{"award-number":["2017YFE0194900"]}]},{"name":"2019 Hunan Postgraduate High-Quality Course Project \u201cMicrowave and Hyperspectral Remote Sensing\u201d","award":["130"],"award-info":[{"award-number":["130"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As a kind of soil-borne epidemic disease, bacterial wilt (BW) is one of the most serious diseases in tomatoes in southern China, which may significantly reduce food quality and the total amount of yield. Hyperspectral remote sensing can detect crop diseases in the early stages and offers potential for BW detection in tomatoes. Tomatoes in southern China are commonly cultivated in greenhouses or bird nets, limiting the application of remote sensing based on natural sunlight. To resolve these issues, we collected the spectrum of tomatoes firstly using the HS-VN1000B Portable Intelligent Spectrometer, which is equipped with a simulated solar light source. We then proposed a tomato BW detection model based on some optimal spectral features. Specifically, these optimal features, including vegetation indexes and principal components (PCs), were extracted by the sequential forward selection (SFS), the simulated annealing (SA), and the genetic algorithm (GA) and were finally fed into the support vector machine (SVM) classifier to detect diseased tomatoes. The results showed that the infected and healthy tomatoes exhibit different spectral characteristics for both leave and stem spectra, especially for near-infrared bands. In addition, the BW detecting model built by the combination of GA and SVM (GA-SVM) achieved the best performance with overall accuracies (OA) of 90.7% for leaves and 92.6% for stems. Compared with the results based on leaves, spectral features of stems provided better accuracy, indicating that the symptom of early infection of BW is more significant in tomato stems than in leaves. Further, the reliability of the GA-SVM tomato stem model was verified in our 2022 experiment with an OA of 88.6% and an F1 score of 0.80. Our study provides an effective means to detect BW disease of tomatoes in the early stages, which could help farmers manage their tomato production and effectively prevent pesticide abuse.<\/jats:p>","DOI":"10.3390\/rs14122882","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T11:45:44Z","timestamp":1655466344000},"page":"2882","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Early Detection of Bacterial Wilt in Tomato with Portable Hyperspectral Spectrometer"],"prefix":"10.3390","volume":"14","author":[{"given":"Yi","family":"Cen","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Ying","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Geographic Sciences, Hunan Normal University, Changsha 410081, China"}]},{"given":"Shunshi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Geographic Sciences, Hunan Normal University, Changsha 410081, China"},{"name":"Hunan Key Laboratory of Geospatial Big Data Mining and Application, Changsha 410081, China"}]},{"given":"Lifu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9890-3598","authenticated-orcid":false,"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Macro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.compag.2016.07.019","article-title":"Crop type mapping in a highly fragmented and heterogeneous agricultural landscape: A case of central Iran using multi-temporal Landsat 8 imagery","volume":"127","author":"Asgarian","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","first-page":"102405","article-title":"Vegetable mapping using fuzzy classification of Dynamic Time Warping distances from time series of Sentinel-1A images","volume":"102","author":"Moola","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","unstructured":"(2022, January 18). Analysis Report on Production and Marketing Situation and Future Prospect of China\u2019s Tomato Industry from 2022 to 2028; R898946. Available online: https:\/\/www.chyxx.com\/research\/202010\/898946.html."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"153","DOI":"10.2480\/agrmet.61.153","article-title":"Predicting Bacterial Wilt Disease of Tomato Plants using Remotely Sensed Thermal Imagery","volume":"61","author":"Chiwaki","year":"2005","journal-title":"J. Agric. Meteorol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105708","DOI":"10.1016\/j.compag.2020.105708","article-title":"Early detection of bacterial wilt in peanut plants through leaf-level hyperspectral and unmanned aerial vehicle data","volume":"177","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.3389\/fpls.2017.01549","article-title":"Bacterial Wilt in China: History, Current Status, and Future Perspectives","volume":"8","author":"Gaofei","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2450","DOI":"10.1016\/S2095-3119(20)63455-4","article-title":"Wheat straw biochar amendment suppresses tomato bacterial wilt caused by Ralstonia solanacearum: Potential effects of rhizosphere organic acids and amino acids","volume":"20","author":"Tian","year":"2021","journal-title":"J. Integr. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9781","DOI":"10.1007\/s00253-018-9347-0","article-title":"Soil acidification amendments change the rhizosphere bacterial community of tobacco in a bacterial wilt affected field","volume":"102","author":"Shen","year":"2018","journal-title":"Appl. Microbiol. Biotechnol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vu, T.T., Kim, H., Tran, V.K., Vu, H.D., Hoang, T.X., Hang, J.W., Choi, Y.H., Jang, K.S., Choi, G.J., and Kim, J.C. (2017). Antibacterial activity of tannins isolated from Sapium baccatum extract and use for control of tomato bacterial wilt. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0181499"},{"key":"ref_10","first-page":"22","article-title":"Research Progress in Controlling Tomato Bacterial Wilt","volume":"1","author":"Wang","year":"2020","journal-title":"China Veg."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1007\/s11119-011-9242-5","article-title":"Detection of bacterial wilt infection caused by Ralstonia solanacearum in potato (Solanum tuberosum L.) through multifractal analysis applied to remotely sensed data","volume":"13","author":"Chavez","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"16564","DOI":"10.1038\/srep16564","article-title":"Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging","volume":"5","author":"Xie","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1146\/annurev-phyto-080417-050100","article-title":"Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art","volume":"56","author":"Mahlein","year":"2018","journal-title":"Annu. Rev. Phytopathol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/JSTARS.2013.2267204","article-title":"Progress in Hyperspectral Remote Sensing Science and Technology in China Over the Past Three Decades","volume":"7","author":"Xue","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1126\/science.228.4704.1147","article-title":"Imaging spectrometry for Earth remote sensing","volume":"228","author":"Goetz","year":"1985","journal-title":"Science"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8249","DOI":"10.1007\/s11356-017-9568-2","article-title":"Reflectance spectroscopy: A novel approach to better understand and monitor the impact of air pollution on Mediterranean plants","volume":"25","author":"Cotrozzi","year":"2018","journal-title":"Environ. Sci. Pollut. Res. Int."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.compag.2010.06.009","article-title":"Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance","volume":"74","author":"Rumpf","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"112012","DOI":"10.1016\/j.rse.2020.112012","article-title":"WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF","volume":"250","author":"Zhong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/07352681003617285","article-title":"Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging","volume":"29","author":"Bock","year":"2010","journal-title":"Crit. Rev. Plant Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, N., Yang, G., Pan, Y., Yang, X., Chen, L., and Zhao, C. (2020). A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades. Remote Sens., 12.","DOI":"10.3390\/rs12193188"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s41348-017-0124-6","article-title":"Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective","volume":"125","author":"Thomas","year":"2017","journal-title":"J. Plant Dis. Prot."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Terentev, A., Dolzhenko, V., Fedotov, A., and Eremenko, D. (2022). Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review. Sensors, 22.","DOI":"10.3390\/s22030757"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"66346","DOI":"10.1109\/ACCESS.2021.3073929","article-title":"A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.compag.2018.12.036","article-title":"Detection of peanut leaf spots disease using canopy hyperspectral reflectance","volume":"156","author":"Chen","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gold, K.M., Townsend, P.A., Chlus, A., Herrmann, I., Couture, J.J., Larson, E.R., and Gevens, A.J. (2020). Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato. Remote Sens., 12.","DOI":"10.3390\/rs12020286"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"515","DOI":"10.5194\/isprs-archives-XLII-3-W6-515-2019","article-title":"Detection of bacterial wilt disease (Pseudomonas solancearum) in Brinjal using hyperspectral remote sensing","volume":"XLII\u20133\/W6","author":"Srivastava","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","article-title":"Plant Disease Detection by Imaging Sensors\u2014Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping","volume":"100","author":"Mahlein","year":"2016","journal-title":"Plant Dis."},{"key":"ref_28","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":"2021","journal-title":"J. For. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1080\/01431161.2010.486416","article-title":"Using class-based feature selection for the classification of hyperspectral data","volume":"32","author":"Maghsoudi","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3562","DOI":"10.3390\/rs5073562","article-title":"Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data","volume":"5","author":"Koedsin","year":"2013","journal-title":"Remote Sens."},{"key":"ref_31","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_32","first-page":"691387","article-title":"Detecting the Early Stage of Phaeosphaeria Leaf Spot Infestations in Maize Crop Using In Situ Hyperspectral Data and Guided Regularized Random Forest Algorithm","volume":"2017","author":"Elhadi","year":"2017","journal-title":"J. Spectrosc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1186\/s13007-018-0349-9","article-title":"Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems","volume":"14","author":"Nagasubramanian","year":"2018","journal-title":"Plant Methods"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Imani, M., and Ghassemian, H. (2015, January 27\u201329). Fast feature selection methods for classification of hyperspectral images. Proceedings of the International Symposium on Telecommunications, Sydney, Australia.","DOI":"10.1109\/ISTEL.2014.7000673"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1109\/36.934069","article-title":"A new search algorithm for feature selection in hyperspectral remote sensing images","volume":"39","author":"Serpico","year":"2001","journal-title":"Geosci. Remote Sens. IEEE Trans."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, Q., Wong, F., and Fung, T. (August, January 28). Comparison Feature Selection Methods for Subtropical Vegetation Classification with Hyperspectral Data. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898541"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1950017","DOI":"10.1142\/S0218001419500174","article-title":"A Review on Dimensionality Reduction Techniques","volume":"33","author":"Huang","year":"2019","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.compag.2019.02.003","article-title":"Optimizing wavelength selection by using informative vectors for parsimonious infrared spectra modelling","volume":"158","author":"Ng","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/S0169-7439(02)00046-1","article-title":"A flexible classification approach with optimal generalisation performance: Support vector machines","volume":"64","author":"Verzakov","year":"2002","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_40","unstructured":"Mercier, G., and Lennon, M. (2003, January 21\u201325). Support vector machines for hyperspectral image classification with spectral-based kernels. Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, Toulouse, France."},{"key":"ref_41","unstructured":"Li, X., and Du, Y. (2006). Description Standard and Data Standard of Tomato Germplasm, China Agricultural Press."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1021\/ac60319a045","article-title":"Smoothing and differentiation of data by simplified least square procedure","volume":"44","author":"Steinier","year":"1972","journal-title":"Anal. Chem."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"104680","DOI":"10.1016\/j.compag.2019.104860","article-title":"Sensitivity of spectral vegetation indices for monitoring water stress in tomato plants","volume":"163","author":"Ihuoma","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.4304\/jsw.6.7.1248-1256","article-title":"Spectral and Wavelet-based Feature Selection with Particle Swarm Optimization for Hyperspectral Classification","volume":"6","author":"Ding","year":"2011","journal-title":"J. Softw."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.biosystemseng.2013.02.007","article-title":"Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging","volume":"115","author":"Zhang","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Marcano-Cedeo, A., Quintanilla-Dom\u00ednguez, J., Cortina-Januchs, M.G., and Andina, D. (2010, January 7\u201310). Feature selection using Sequential Forward Selection and classification applying Artificial Metaplasticity Neural Network. Proceedings of the IECON 2010\u201436th Annual Conference on IEEE Industrial Electronics Society, Glendale, AZ, USA.","DOI":"10.1109\/IECON.2010.5675075"},{"key":"ref_47","first-page":"31","article-title":"Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning","volume":"4","author":"Gao","year":"2020","journal-title":"Artif. Intell. Agric."},{"key":"ref_48","first-page":"4598","article-title":"Optimization by Simulated Annealing","volume":"13","author":"Kirkpatrick","year":"1983","journal-title":"Science"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.patcog.2009.06.009","article-title":"Feature subset selection in large dimensionality domains","volume":"43","author":"Gheyas","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s12524-016-0577-2","article-title":"Band Selection and Dimension Estimation for Hyperspectral Imagery\u2014A New Approach Based on Invasive Weed Optimization","volume":"45","author":"Pahlavani","year":"2016","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_51","unstructured":"Maghsoudi, Y., Alimohammadi, A., Zoej, M.J.V., and Mojaradi, B. (2005, January 7\u201311). Application of Feature Selection and Classifier Ensembles for the Classification of Hyperspectral Data. Proceedings of the 26th Asian Conference on Remote Sensing and 2nd Asian Space Conference, Hanoi, Vietnam."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1109\/TGRS.2004.827257","article-title":"A relative evaluation of multiclass image classification by support vector machines","volume":"42","author":"Foody","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/JSTARS.2013.2262926","article-title":"A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification","volume":"7","author":"Kuo","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"119585","DOI":"10.1016\/j.saa.2021.119585","article-title":"Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM","volume":"253","author":"Yang","year":"2021","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1109\/TGRS.2009.2039484","article-title":"Feature Selection for Classification of Hyperspectral Data by SVM","volume":"48","author":"Pal","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, P., Zhang, Y., Zhang, L., and Wei, W. (2016, January 19\u201321). A multi-label Hyperspectral image classification method with deep learning features. Proceedings of the International Conference on Internet Multimedia Computing and Service, Xi\u2019an, China.","DOI":"10.1145\/3007669.3007742"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Vicente Garc\u00eda, R.A.M., and S\u00e1nchez, J.S. (2009, January 10\u201312). Index of Balanced Accuracy: A Performance Measure for Skewed Class Distributions. Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis, Povoa de Varzim, Portugal.","DOI":"10.1007\/978-3-642-02172-5_57"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5962","DOI":"10.1111\/1462-2920.15535","article-title":"Unravelling physiological signatures of tomato bacterial wilt and xylem metabolites exploited by Ralstonia solanacearum","volume":"23","author":"Gerlin","year":"2021","journal-title":"Environ. Microbiol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s11119-012-9262-9","article-title":"A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring","volume":"13","author":"Lebourgeois","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_60","first-page":"35","article-title":"Identification and level discrimination of waterlogging stress in winter wheat using hyperspectral remote sensing","volume":"3","author":"Feifei","year":"2021","journal-title":"Smart Agric."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/TNNLS.2013.2248094","article-title":"PCA feature extraction for change detection in multidimensional unlabeled data","volume":"25","author":"Kuncheva","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Dong, Y., Huang, W., Liu, L., and Ma, H. (2021). Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size. Remote Sens., 13.","DOI":"10.3390\/rs13132437"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Khan, I.H., Liu, H., Li, W., Cao, A., Wang, X., Liu, H., Cheng, T., Tian, Y., Zhu, Y., and Cao, W. (2021). Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat. Remote Sens., 13.","DOI":"10.3390\/rs13183612"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Guo, A., Huang, W., Dong, Y., Ye, H., Ma, H., Liu, B., Wu, W., Ren, Y., Ruan, C., and Geng, Y. (2021). Wheat Yellow Rust Detection Using UAV-Based Hyperspectral Technology. Remote Sens., 13.","DOI":"10.3390\/rs13010123"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2882\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:33:01Z","timestamp":1760139181000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2882"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,16]]},"references-count":64,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122882"],"URL":"https:\/\/doi.org\/10.3390\/rs14122882","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,16]]}}}