{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:01:32Z","timestamp":1784300492976,"version":"3.55.0"},"reference-count":65,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32101621"],"award-info":[{"award-number":["32101621"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62061041"],"award-info":[{"award-number":["62061041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31960503"],"award-info":[{"award-number":["31960503"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022CB001-05"],"award-info":[{"award-number":["2022CB001-05"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021BB023-02"],"award-info":[{"award-number":["2021BB023-02"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["TDZKSS202345"],"award-info":[{"award-number":["TDZKSS202345"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["TDGRI202256"],"award-info":[{"award-number":["TDGRI202256"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Bingtuan Science and Technology Program","award":["32101621"],"award-info":[{"award-number":["32101621"]}]},{"name":"Bingtuan Science and Technology Program","award":["62061041"],"award-info":[{"award-number":["62061041"]}]},{"name":"Bingtuan Science and Technology Program","award":["31960503"],"award-info":[{"award-number":["31960503"]}]},{"name":"Bingtuan Science and Technology Program","award":["2022CB001-05"],"award-info":[{"award-number":["2022CB001-05"]}]},{"name":"Bingtuan Science and Technology Program","award":["2021BB023-02"],"award-info":[{"award-number":["2021BB023-02"]}]},{"name":"Bingtuan Science and Technology Program","award":["TDZKSS202345"],"award-info":[{"award-number":["TDZKSS202345"]}]},{"name":"Bingtuan Science and Technology Program","award":["TDGRI202256"],"award-info":[{"award-number":["TDGRI202256"]}]},{"name":"Tarim University President\u2019s Fund","award":["32101621"],"award-info":[{"award-number":["32101621"]}]},{"name":"Tarim University President\u2019s Fund","award":["62061041"],"award-info":[{"award-number":["62061041"]}]},{"name":"Tarim University President\u2019s Fund","award":["31960503"],"award-info":[{"award-number":["31960503"]}]},{"name":"Tarim University President\u2019s Fund","award":["2022CB001-05"],"award-info":[{"award-number":["2022CB001-05"]}]},{"name":"Tarim University President\u2019s Fund","award":["2021BB023-02"],"award-info":[{"award-number":["2021BB023-02"]}]},{"name":"Tarim University President\u2019s Fund","award":["TDZKSS202345"],"award-info":[{"award-number":["TDZKSS202345"]}]},{"name":"Tarim University President\u2019s Fund","award":["TDGRI202256"],"award-info":[{"award-number":["TDGRI202256"]}]},{"name":"Graduate Scientific Research Innovation project of Tarim University","award":["32101621"],"award-info":[{"award-number":["32101621"]}]},{"name":"Graduate Scientific Research Innovation project of Tarim University","award":["62061041"],"award-info":[{"award-number":["62061041"]}]},{"name":"Graduate Scientific Research Innovation project of Tarim University","award":["31960503"],"award-info":[{"award-number":["31960503"]}]},{"name":"Graduate Scientific Research Innovation project of Tarim University","award":["2022CB001-05"],"award-info":[{"award-number":["2022CB001-05"]}]},{"name":"Graduate Scientific Research Innovation project of Tarim University","award":["2021BB023-02"],"award-info":[{"award-number":["2021BB023-02"]}]},{"name":"Graduate Scientific Research Innovation project of Tarim University","award":["TDZKSS202345"],"award-info":[{"award-number":["TDZKSS202345"]}]},{"name":"Graduate Scientific Research Innovation project of Tarim University","award":["TDGRI202256"],"award-info":[{"award-number":["TDGRI202256"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In order to address the challenge of early detection of cotton verticillium wilt disease, naturally infected cotton plants in the field, which were divided into five categories based on the degree of disease severity, have been investigated in this study. Canopies of infected cotton plants were analyzed with spectral data measured, and various preprocessing techniques, including multiplicative scatter correction (MSC) and MSC-continuous wavelet analysis algorithms, were used to predict the disease severity. With a combination of support vector machine (SVM) models with such optimization algorithms as genetic algorithm (GA), grid search (GS), particle swarm optimization (PSO), and grey wolf optimizer (GWO), a grading model of cotton verticillium wilt disease was established in this study. The study results show that the MSC-PSO-SVM model outperforms the other three models in terms of classification accuracy, and the accuracy, macro precision, macro recall, and macro F1-score of this model are 80%, 81.26%, 80%, and 79.57%, respectively. Among those eight models constructed on the basis of continuous wavelet analyses using mexh and db3, the MSC-db3(23)-PSO-SVM and MSC-db3(23)-GWO-SVM models perform best, with the latter having a shorter running time. An overall evaluation shows that the MSC-db3(23)-GWO-SVM model is an optimal model, with values of its accuracy, macro precision, macro recall, and macro F1-score indicators being 91.2%, 92.02%, 91.2%, and 91.16%, respectively. Moreover, under this model, the prediction accuracy on disease levels 1 and 5 has achieved the highest rate of 100%, with a prediction accuracy rate of 88% on disease level 2 and the lowest prediction accuracy rate of 84% on both disease levels 3 and 4. These results demonstrate that it is effective to use spectral technology in classifying the cotton verticillium wilt disease and satisfying the needs of field detection and grading. This study provides a new approach for the detection and grading of cotton verticillium wilt disease and offered a theoretical basis for early prevention, precise drug application, and instrument development for the disease.<\/jats:p>","DOI":"10.3390\/rs15133373","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:49:27Z","timestamp":1688345367000},"page":"3373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Detection of Cotton Verticillium Wilt Disease Severity Based on Hyperspectrum and GWO-SVM"],"prefix":"10.3390","volume":"15","author":[{"given":"Nannan","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar 843300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar 843300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Shang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar 843300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar 843300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xintao","family":"Yuan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar 843300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0095-558X","authenticated-orcid":false,"given":"Tiecheng","family":"Bai","sequence":"additional","affiliation":[{"name":"Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar 843300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,1]]},"reference":[{"key":"ref_1","first-page":"933","article-title":"Assessing the Severity of Cotton Verticillium Wilt Disease from in Situ Canopy Images and Spectra Using Convolutional Neural Networks","volume":"12","author":"Kang","year":"2022","journal-title":"Crop J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1016\/j.matpr.2021.08.036","article-title":"Suppression of Verticillium Wilt of Cotton through Liquid Material and Antagonistic Fungal Strains under Natural Field Conditions","volume":"60","author":"Kaur","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2706","DOI":"10.1080\/01431161.2011.619586","article-title":"Evaluating the Severity Level of Cotton Verticillium Using Spectral Signature Analysis","volume":"33","author":"Chen","year":"2012","journal-title":"J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1174281","DOI":"10.3389\/fpls.2023.1174281","article-title":"Interactions between Verticillium Dahliae and Cotton: Pathogenic Mechanism and Cotton Resistance Mechanism to Verticillium Wilt","volume":"14","author":"Zhu","year":"2023","journal-title":"Front. Plant Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/S1671-2927(08)60053-X","article-title":"Spectrum Characteristics of Cotton Canopy Infected with Verticillium Wilt and Applications","volume":"7","author":"Chen","year":"2008","journal-title":"Agric. Sci. China"},{"key":"ref_6","first-page":"1095","article-title":"Screening of bacteria antagonistic against soil-borne cotton Verticillium wilt and their biological effects on the soil-cotton system","volume":"45","author":"Zhang","year":"2008","journal-title":"Acta Pedol. Sin."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s42483-020-00049-8","article-title":"From Visual Estimates to Fully Automated Sensor-Based Measurements of Plant Disease Severity: Status and Challenges for Improving Accuracy","volume":"2","author":"Bock","year":"2020","journal-title":"Phytopathol. Res."},{"key":"ref_8","unstructured":"Chen, B. (2010). Study on Monitoring Cotton Infected with Verticillium Wilt Based on Multi-platform Remote Sensing. [Ph.D. Thesis, Shihezi University]."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"577063","DOI":"10.3389\/fpls.2020.577063","article-title":"Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods","volume":"11","author":"Feng","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"828454","DOI":"10.3389\/fpls.2022.828454","article-title":"Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning","volume":"13","author":"Feng","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5098","DOI":"10.1038\/s41598-022-08969-5","article-title":"Surveying Soil-Borne Disease Development on Wild Rocket Salad Crop by Proximal Sensing Based on High-Resolution Hyperspectral Features","volume":"12","author":"Galieni","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.1016\/j.cj.2022.07.009","article-title":"Quantifying the Effects of Stripe Rust Disease on Wheat Canopy Spectrum Based on Eliminating Non-Physiological Stresses","volume":"10","author":"Jing","year":"2022","journal-title":"Crop J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1631\/jzus.2007.B0738","article-title":"Characterizing and Estimating Rice Brown Spot Disease Severity Using Stepwise Regression, Principal Component Regression and Partial Least-Square Regression","volume":"8","author":"Liu","year":"2007","journal-title":"J. Zhejiang Univ. Sci. B"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"879668","DOI":"10.3389\/fpls.2022.879668","article-title":"Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm","volume":"13","author":"Zhao","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_15","first-page":"1","article-title":"Research progress of crop diseases and pests monitoring based on remote sensing","volume":"28","author":"Zhang","year":"2012","journal-title":"Trans. CSAE"},{"key":"ref_16","first-page":"261","article-title":"Spectral Characteristics Analysis of Cotton Verticillium Wilt Canopy and Establishment of Its Severity Estimation Model","volume":"43","author":"Chen","year":"2020","journal-title":"J. Xinjiang Agric. Univ."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.cropro.2012.12.002","article-title":"Detection of Powdery Mildew in Two Winter Wheat Cultivars Using Canopy Hyperspectral Reflectance","volume":"45","author":"Cao","year":"2013","journal-title":"Crop Prot."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"126607","DOI":"10.1016\/j.eja.2022.126607","article-title":"Accurate Modeling of Vertical Leaf Nitrogen Distribution in Summer Maize Using in Situ Leaf Spectroscopy via CWT and PLS-Based Approaches","volume":"140","author":"Li","year":"2022","journal-title":"Eur. J. Agron."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s11119-012-9283-4","article-title":"Evaluation of Spectral Indices and Continuous Wavelet Analysis to Quantify Aphid Infestation in Wheat","volume":"14","author":"Luo","year":"2013","journal-title":"Precis. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mustafa, G., Zheng, H., Khan, I.H., Tian, L., Jia, H., Li, G., Cheng, T., Tian, Y., Cao, W., and Zhu, Y. (2022). Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning. Remote Sens., 14.","DOI":"10.3390\/rs14122784"},{"key":"ref_22","first-page":"145","article-title":"Quantitative Identification of Crop Disease and Nitrogen-Water Stress in Winter Wheat Using Continuous Wavelet Analysis","volume":"11","author":"Huang","year":"2018","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/JAS.2021.1004129","article-title":"A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends","volume":"8","author":"Tang","year":"2021","journal-title":"IEEE\/CAA J. Autom. Sinica"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1109\/LGRS.2006.888847","article-title":"Retrieval of Fresh Leaf Fuel Moisture Content Using Genetic Algorithm Partial Least Squares (GA-PLS) Modeling","volume":"4","author":"Li","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1080\/01431161.2019.1654142","article-title":"Extracting Soil Salinization Information with a Fractional-Order Filtering Algorithm and Grid-Search Support Vector Machine (GS-SVM) Model","volume":"41","author":"Wang","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103220","DOI":"10.1016\/j.infrared.2020.103220","article-title":"Vis-NIR Hyperspectral Imaging for the Classification of Bacterial Foodborne Pathogens Based on Pixel-Wise Analysis and a Novel CARS-PSO-SVM Model","volume":"105","author":"Bonah","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.advengsoft.2017.01.004","article-title":"Grasshopper Optimisation Algorithm: Theory and Application","volume":"105","author":"Saremi","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6372","DOI":"10.1007\/s10489-022-03791-y","article-title":"Enhanced Discrete Dragonfly Algorithm for Solving Four-Color Map Problems","volume":"53","author":"Zhong","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey Wolf Optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_30","first-page":"3147","article-title":"Rapidly Detection of Total Nitrogen and Phosphorus Content in Water by Surface Enhanced Raman Spectroscopy and GWO-SVR Algorithm","volume":"41","author":"Zhang","year":"2021","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"131013","DOI":"10.1016\/j.foodchem.2021.131013","article-title":"Soluble Solid Content and Firmness Index Assessment and Maturity Discrimination of Malus Micromalus Makino Based on Near-Infrared Hyperspectral Imaging","volume":"370","author":"Gao","year":"2022","journal-title":"Food Chem."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112192","DOI":"10.1016\/j.foodres.2022.112192","article-title":"Strategies for the Content Determination of Capsaicin and the Identification of Adulterated Pepper Powder Using a Hand-Held near-Infrared Spectrometer","volume":"163","author":"Wu","year":"2023","journal-title":"Food Res. Int."},{"key":"ref_33","first-page":"3291","article-title":"Classification of Qianxi Tomatoes by Visible\/Near Infrared Spectroscopy Combined with GMO-SVM","volume":"42","author":"Zhang","year":"2022","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"15937","DOI":"10.1038\/s41598-022-20299-0","article-title":"Estimating Leaf Area Index of Maize Using UAV-Based Digital Imagery and Machine Learning Methods","volume":"12","author":"Du","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.3389\/fpls.2018.01102","article-title":"On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties","volume":"9","author":"Diago","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, W., Ma, Y., Li, J., and Ma, B. (2022). The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM. Molecules, 27.","DOI":"10.3390\/molecules27134091"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"119666","DOI":"10.1016\/j.saa.2021.119666","article-title":"Application of Long-Wave near Infrared Hyperspectral Imaging for Determination of Moisture Content of Single Maize Seed","volume":"254","author":"Wang","year":"2021","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1031030","DOI":"10.3389\/fpls.2022.1031030","article-title":"A Non-Destructive Testing Method for Early Detection of Ginseng Root Diseases Using Machine Learning Technologies Based on Leaf Hyperspectral Reflectance","volume":"13","author":"Zhao","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4125","DOI":"10.1038\/s41598-017-04501-2","article-title":"Hyperspectral Imaging for Presymptomatic Detection of Tobacco Disease with Successive Projections Algorithm and Machine-Learning Classifiers","volume":"7","author":"Zhu","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1186\/s13007-022-00955-2","article-title":"Hyperspectral Imaging-Based Classification of Rice Leaf Blast Severity over Multiple Growth Stages","volume":"18","author":"Zhang","year":"2022","journal-title":"Plant Methods"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, J., Fang, Y., Chu, G., Yan, H., Hu, L., and Huang, L. (2020). Identification of Leaf-Scale Wheat Powdery Mildew (Blumeria graminis f. Sp. tritici) Combining Hyperspectral Imaging and an SVM Classifier. Plants, 9.","DOI":"10.3390\/plants9080936"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, S., Yu, H., Sui, Y., Zhou, H., Zhang, J., Kong, L., Dang, J., and Zhang, L. (2021). Classification of Soybean Frogeye Leaf Spot Disease Using Leaf Hyperspectral Reflectance. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0257008"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"117983","DOI":"10.1016\/j.saa.2019.117983","article-title":"Spectroscopy Based Novel Spectral Indices, PCA- and PLSR-Coupled Machine Learning Models for Salinity Stress Phenotyping of Rice","volume":"229","author":"Das","year":"2020","journal-title":"Spectrochim. Acta. A Mol. Biomol. Spectrosc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"120439","DOI":"10.1016\/j.saa.2021.120439","article-title":"Study on the Identification of Resistance of Rice Blast Based on near Infrared Spectroscopy","volume":"266","author":"He","year":"2022","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.compeleceng.2019.04.011","article-title":"Identification of Plant Leaf Diseases Using a Nine-Layer Deep Convolutional Neural Network","volume":"76","author":"Geetharamani","year":"2019","journal-title":"Comput. Electr. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lu, J., Tan, L., and Jiang, H. (2021). Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification. Agriculture, 11.","DOI":"10.3390\/agriculture11080707"},{"key":"ref_47","first-page":"898","article-title":"Early Detection and Identification of Rice Blast Based on Hyperspectral Image","volume":"41","author":"Kang","year":"2021","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_48","first-page":"828","article-title":"Early Detection of Downy Mildew on Grape Leaves Using Multicolor Fluorescence Imaging and Model SVM","volume":"41","author":"Zhang","year":"2021","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_49","first-page":"1220","article-title":"Prediction Model of Rice Panicles Blast Disease Degree Based on Canopy Hyperspectral Reflectance","volume":"41","author":"Han","year":"2021","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_50","first-page":"2129","article-title":"Research on Tea Cephaleuros Virescens Kunze Model Based on Chlorophyll Fluorescence Spectroscopy","volume":"41","author":"Liu","year":"2021","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"106546","DOI":"10.1016\/j.compag.2021.106546","article-title":"Support Vector Machine in Precision Agriculture: A Review","volume":"191","author":"Kok","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Li, F., Wang, L., Liu, J., Wang, Y., and Chang, Q. (2019). Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis. Remote Sens., 11.","DOI":"10.3390\/rs11111331"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2003.09.004","article-title":"Towards Universal Broad Leaf Chlorophyll Indices Using PROSPECT Simulated Database and Hyperspectral Reflectance Measurements","volume":"89","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_54","first-page":"611","article-title":"Wavelet transform combined with spa to optimize the near-infrared analysis model of caffeine in tea","volume":"37","author":"Zhao","year":"2021","journal-title":"J. Anal. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ding, Y., Yan, Y., Li, J., Chen, X., and Jiang, H. (2022). Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM. Foods, 11.","DOI":"10.3390\/foods11111658"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Nkongolo, M., Van Deventer, J.P., Kasongo, S.M., Zahra, S.R., and Kipongo, J. (2022). A Cloud Based Optimization Method for Zero-Day Threats Detection Using Genetic Algorithm and Ensemble Learning. Electronics, 11.","DOI":"10.3390\/electronics11111749"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1016\/j.asoc.2007.10.007","article-title":"A Distributed PSO\u2013SVM Hybrid System with Feature Selection and Parameter Optimization","volume":"8","author":"Huang","year":"2008","journal-title":"Appl. Soft Comput."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"105160","DOI":"10.1016\/j.compag.2019.105160","article-title":"Evaluating Photosynthetic Pigment Contents of Maize Using UVE-PLS Based on Continuous Wavelet Transform","volume":"169","author":"Wang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_60","first-page":"162","article-title":"Monitoring Model of Winter Wheat Take-all Based on UAV Hyperspectral Imaging","volume":"50","author":"Guo","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Song, L., Liang, Q., Chen, H., Hu, H., Luo, Y., and Luo, Y. (2022). A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm. Sensors, 23.","DOI":"10.3390\/s23010272"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1186\/s13007-018-0344-1","article-title":"Estimation of Area- and Mass-Based Leaf Nitrogen Contents of Wheat and Rice Crops from Water-Removed Spectra Using Continuous Wavelet Analysis","volume":"14","author":"Li","year":"2018","journal-title":"Plant Methods"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.12.013","article-title":"PROCWT: Coupling PROSPECT with Continuous Wavelet Transform to Improve the Retrieval of Foliar Chemistry from Leaf Bidirectional Reflectance Spectra","volume":"206","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ma, H., Huang, W., Dong, Y., Liu, L., and Guo, A. (2021). Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight. Remote Sens., 13.","DOI":"10.3390\/rs13153024"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Chen, X., Li, F., and Chang, Q. (2023). Combination of Continuous Wavelet Transform and Successive Projection Algorithm for the Estimation of Winter Wheat Plant Nitrogen Concentration. Remote Sens., 15.","DOI":"10.3390\/rs15040997"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3373\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:04:35Z","timestamp":1760126675000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,1]]},"references-count":65,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133373"],"URL":"https:\/\/doi.org\/10.3390\/rs15133373","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,1]]}}}