{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:07:19Z","timestamp":1777939639039,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,10,27]],"date-time":"2017-10-27T00:00:00Z","timestamp":1509062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Striped stem-borer (SSB) infestation is one of the most serious sources of damage to rice growth. A rapid and non-destructive method of early SSB detection is essential for rice-growth protection. In this study, hyperspectral imaging combined with chemometrics was used to detect early SSB infestation in rice and identify the degree of infestation (DI). Visible\/near-infrared hyperspectral images (in the spectral range of 380 nm to 1030 nm) were taken of the healthy rice plants and infested rice plants by SSB for 2, 4, 6, 8 and 10 days. A total of 17 characteristic wavelengths were selected from the spectral data extracted from the hyperspectral images by the successive projection algorithm (SPA). Principal component analysis (PCA) was applied to the hyperspectral images, and 16 textural features based on the gray-level co-occurrence matrix (GLCM) were extracted from the first two principal component (PC) images. A back-propagation neural network (BPNN) was used to establish infestation degree evaluation models based on full spectra, characteristic wavelengths, textural features and features fusion, respectively. BPNN models based on a fusion of characteristic wavelengths and textural features achieved the best performance, with classification accuracy of calibration and prediction sets over 95%. The accuracy of each infestation degree was satisfactory, and the accuracy of rice samples infested for 2 days was slightly low. In all, this study indicated the feasibility of hyperspectral imaging techniques to detect early SSB infestation and identify degrees of infestation.<\/jats:p>","DOI":"10.3390\/s17112470","type":"journal-article","created":{"date-parts":[[2017,10,27]],"date-time":"2017-10-27T11:33:28Z","timestamp":1509104008000},"page":"2470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Fast Detection of Striped Stem-Borer (Chilo suppressalis Walker) Infested Rice Seedling Based on Visible\/Near-Infrared Hyperspectral Imaging System"],"prefix":"10.3390","volume":"17","author":[{"given":"Yangyang","family":"Fan","sequence":"first","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}]},{"given":"Zhengjun","family":"Qiu","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2842-170X","authenticated-orcid":false,"given":"Jiyu","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6760-3154","authenticated-orcid":false,"given":"Chu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6752-1757","authenticated-orcid":false,"given":"Yong","family":"He","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1007\/s11434-009-0645-x","article-title":"Review and prospect of transgenic rice research","volume":"54","author":"Chen","year":"2009","journal-title":"Chin. Sci. Bull."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1093\/database\/bau065","article-title":"Chilodb: A genomic and transcriptome database for an important rice insect pest chilo suppressalis","volume":"2014","author":"Yin","year":"2014","journal-title":"Database"},{"key":"ref_3","unstructured":"Dale, D. (1994). Insect Pests of the Rice Plant\u2014Their Biology and Ecology, Wiley."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1080\/09670878509371002","article-title":"Chemical control of the striped stem borer, chilo-suppressalis (walker) in rice","volume":"31","author":"Fademi","year":"1985","journal-title":"Trop. Pest Manag."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.foodchem.2017.05.064","article-title":"Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content","volume":"235","author":"Sun","year":"2017","journal-title":"Food Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.jfoodeng.2017.05.029","article-title":"Detection of blood in fish muscle by constrained spectral unmixing of hyperspectral images","volume":"212","author":"Skjelvareid","year":"2017","journal-title":"J. Food Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.4161\/psb.21663","article-title":"Mechanisms of plant defense against insect herbivores","volume":"7","author":"War","year":"2012","journal-title":"Plant Signal. Behav."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1046\/j.1570-7458.2002.01031.x","article-title":"Dynamic change in photosynthetic pigments and chlorophyll degradation elicited by cereal aphid feeding","volume":"105","author":"Ni","year":"2002","journal-title":"Entomol. Exp. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s11119-007-9038-9","article-title":"Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging","volume":"8","author":"Huang","year":"2007","journal-title":"Precis. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1007\/s11119-014-9367-4","article-title":"Detection of brown planthopper infestation based on spad and spectral data from rice under different rates of nitrogen fertilizer","volume":"16","author":"Huang","year":"2015","journal-title":"Precis. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.compag.2009.03.003","article-title":"Differentiating stress induced by greenbugs and russian wheat aphids in wheat using remote sensing","volume":"67","author":"Yang","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.compag.2013.06.007","article-title":"Reflectance-based assessment of spider mite \u201cbio-response\u201d to maize leaves and plant potassium content in different irrigation regimes","volume":"97","author":"Nansen","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Rupanagudi, S.R., Ranjani, B.S., Nagaraj, P., Bhat, V.G., and Thippeswamy, G. (2015, January 15\u201317). A Novel Cloud Computing Based Smart Farming System for Early Detection of Borer Insects in Tomatoes. Proceedings of the International Conference on Communication, Information & Computing Technology, Mumbai, India.","DOI":"10.1109\/ICCICT.2015.7045722"},{"key":"ref_15","first-page":"77","article-title":"Early pest identification in agricultural crops using image processing techniques","volume":"2","author":"Bhadane","year":"2013","journal-title":"Int. J. Electr. Electron. Comput. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huddar, S.R., Gowri, S., Keerthana, K., Vasanthi, S., and Rupanagudi, S.R. (2012, January 26\u201328). Novel Algorithm for Segmentation and Automatic Identification of Pests on Plants Using Image Processing. Proceedings of the Third International Conference on Computing Communication & Networking Technologies (ICCCNT), Coimbatore, India.","DOI":"10.1109\/ICCCNT.2012.6396012"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s13007-017-0233-z","article-title":"Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress","volume":"13","author":"Lowe","year":"2017","journal-title":"Plant Methods"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.scitotenv.2016.08.014","article-title":"Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance","volume":"578","author":"Sytar","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Thomas, S., Kuska, M.T., Bohnenkamp, D., Brugger, A., Alisaac, E., Wahabzada, M., Behmann, J., and Mahlein, A.-K. (2017). Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective. J. Plant Dis. Prot., 1\u201316.","DOI":"10.1007\/s41348-017-0124-6"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.13031\/trans.11886","article-title":"Early detection of aphid (myzus persicae) infestation on chinese cabbage by hyperspectral imaging and feature extraction","volume":"60","author":"Zhao","year":"2017","journal-title":"Trans. ASABE"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"311","DOI":"10.13031\/aea.32.11444","article-title":"A novel method for detection of pieris rapae larvae on cabbage leaves using nir hyperspectral imaging","volume":"32","author":"Wu","year":"2016","journal-title":"Appl. Eng. Agric."},{"key":"ref_22","first-page":"2907","article-title":"The rice transcription factor wrky53 suppresses herbivore-induced defenses by acting as a negative feedback modulator of mitogen-activated protein kinase activity","volume":"169","author":"Hu","year":"2015","journal-title":"Plant Physiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0003-2670(01)01281-8","article-title":"Monitoring ethylene content in heterophasic copolymers by near-infrared spectroscopy-standardisation of the calibration model","volume":"445","author":"Macho","year":"2001","journal-title":"Anal. Chim. Acta"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"smc-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.jfoodeng.2016.07.005","article-title":"Assessment of intramuscular fat content of pork using nir hyperspectral images of rib end","volume":"193","author":"Hui","year":"2017","journal-title":"J. Food Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3417","DOI":"10.1080\/01431160701601782","article-title":"Analysis of co-occurrence and discrete wavelet transform textures for differentiation of forest and non-forest vegetation in very high resolution optical-sensor imagery","volume":"29","author":"Tetuko","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4709","DOI":"10.3390\/s8084709","article-title":"Estimation of tree size diversity using object oriented texture analysis and aster imagery","volume":"8","author":"Ozdemir","year":"2008","journal-title":"Sensors"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.1590\/S0103-50532007000800021","article-title":"Cross-validation for the selection of spectral variables using the successive projections algorithm","volume":"18","author":"Galvao","year":"2007","journal-title":"J. Braz. Chem. Soc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.lwt.2016.10.006","article-title":"Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches","volume":"75","author":"Sun","year":"2017","journal-title":"LWT Food Sci. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0169-7439(01)00119-8","article-title":"The successive projections algorithm for variable selection in spectroscopic multicomponent analysis","volume":"57","author":"Araujo","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.chemolab.2007.12.004","article-title":"A variable elimination method to improve the parsimony of mlr models using the successive projections algorithm","volume":"92","author":"Fragoso","year":"2008","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, C., Ye, H., Liu, F., He, Y., Kong, W., and Sheng, K. (2016). Determination and visualization of pH values in anaerobic digestion of water hyacinth and rice straw mixtures using hyperspectral imaging with wavelet transform denoising and variable selection. Sensors, 16.","DOI":"10.3390\/s16020244"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.jfoodeng.2015.04.008","article-title":"A comparative study for improving prediction of total viable count in beef based on hyperspectral scattering characteristics","volume":"162","author":"Tao","year":"2015","journal-title":"J. Food Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1080\/10798587.2015.1015772","article-title":"Diagnosis of ctv-infected leaves using hyperspectral imaging","volume":"21","author":"Guo","year":"2015","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_35","first-page":"834","article-title":"Improving the performance of backpropagation neural network algorithm for image compression\/decompression system","volume":"6","year":"2010","journal-title":"J. Comput. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"27790","DOI":"10.1038\/srep27790","article-title":"Hyperspectral imaging for determining pigment contents in cucumber leaves in response to angular leaf spot disease","volume":"6","author":"Zhao","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.compag.2011.09.012","article-title":"Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (hemiptera: Cicadellidae)","volume":"79","author":"Prabhakar","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1007\/s10340-003-0001-x","article-title":"Interactions between the striped stem borer chilo suppressalis (walk.) (lep., pyralidae) larvae and rice plants in response to nitrogen fertilization","volume":"76","author":"Jiang","year":"2003","journal-title":"J. Pest Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/0261-2194(95)00102-6","article-title":"Mechanisms of compensation of rice plants to yellow stem borer scirpophaga incertulas (walker) injury","volume":"15","author":"Rubia","year":"1996","journal-title":"Crop Prot."},{"key":"ref_40","first-page":"20150202","article-title":"Principal component analysis: A review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S1360-1385(98)01213-8","article-title":"Visible and near-infrared reflectance techniques for diagnosing plant physiological status","volume":"3","author":"Penuelas","year":"1998","journal-title":"Trends Plant Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.jfoodeng.2016.01.002","article-title":"Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine","volume":"179","author":"Zhang","year":"2016","journal-title":"J. Food Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xie, C.Q., and He, Y. (2016). Spectrum and image texture features analysis for early blight disease detection on eggplant leaves. Sensors, 16.","DOI":"10.3390\/s16050676"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.postharvbio.2016.07.007","article-title":"Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data","volume":"121","author":"Fan","year":"2016","journal-title":"Postharvest Biol. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/014311698215748","article-title":"Review article multisensor image fusion in remote sensing: Concepts, methods and applications","volume":"19","author":"Pohl","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.foodchem.2014.03.096","article-title":"Combination of spectra and texture data of hyperspectral imaging for prediction of ph in salted meat","volume":"160","author":"Liu","year":"2014","journal-title":"Food Chem."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/11\/2470\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:41Z","timestamp":1760208521000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/11\/2470"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,27]]},"references-count":47,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2017,11]]}},"alternative-id":["s17112470"],"URL":"https:\/\/doi.org\/10.3390\/s17112470","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10,27]]}}}