{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T05:48:01Z","timestamp":1788846481411,"version":"build-2803163510"},"reference-count":67,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP22784689"],"award-info":[{"award-number":["AP22784689"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>The spectral characteristics of harmful insect pests in wheat fields were characterised using hyperspectral imaging for the first time. The analysis of spectral profiles revealed that reflectance is determined by the structure of the insect\u2019s chitin and the colouration of its body surface. Insects with lighter or more vivid colours (white, yellow, or green) showed higher reflectance values compared to those with predominantly dark pigmentation. Reflectance was also influenced by the presence of wings, surface roughness, and the age of the insect. Each species exhibited distinct spectral patterns that allowed for differentiation not only from other insect species but also from the plant background. A classification model using PLS-DA was developed and demonstrated high accuracy in identifying 12 pest species, confirming the strong potential of hyperspectral imaging for species-level classification. The results validate the PLS-DA method for differentiating insects based on spectral characteristics and underscore the reliability of this approach for automated monitoring systems to detect phytophagous pests in crop fields. This technology could reduce insecticide use by 30\u201340% through targeted application. The research has both scientific and economic significance, laying the groundwork for integrating machine learning and computer vision into agricultural monitoring. It supports the advancement of precision farming and contributes to improved global food security.<\/jats:p>","DOI":"10.3390\/biology14121715","type":"journal-article","created":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T13:03:02Z","timestamp":1764594182000},"page":"1715","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Hyperspectral Imaging and Machine Learning for Automated Pest Identification in Cereal Crops"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3551-5007","authenticated-orcid":false,"given":"Rimma M.","family":"Ualiyeva","sequence":"first","affiliation":[{"name":"Department of Biology and Ecology, Toraighyrov University, Pavlodar 140008, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3838-7656","authenticated-orcid":false,"given":"Mariya M.","family":"Kaverina","sequence":"additional","affiliation":[{"name":"Department of Biology and Ecology, Toraighyrov University, Pavlodar 140008, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3947-5252","authenticated-orcid":false,"given":"Anastasiya V.","family":"Osipova","sequence":"additional","affiliation":[{"name":"Department of Biology and Ecology, Toraighyrov University, Pavlodar 140008, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9878-8224","authenticated-orcid":false,"given":"Alina A.","family":"Faurat","sequence":"additional","affiliation":[{"name":"Department of Geography and Tourism, Toraighyrov University, Pavlodar 140008, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5813-8331","authenticated-orcid":false,"given":"Sayan B.","family":"Zhangazin","sequence":"additional","affiliation":[{"name":"Department of Biotechnology and Microbiology, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1278-5207","authenticated-orcid":false,"given":"Nurgul N.","family":"Iksat","sequence":"additional","affiliation":[{"name":"Department of Biotechnology and Microbiology, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,1]]},"reference":[{"key":"ref_1","unstructured":"Palmquist, J. (2020). Detecting Defects on Cheese Using Hyperspectral Image Analysis. [Master\u2019s Thesis, Ume\u00e5 University]."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sajib, M.M.H., and Sayem, A.S.M. (2025). Innovations in Sensor-Based Systems and Sustainable Energy Solutions for Smart Agriculture: A Review. Encyclopedia, 5.","DOI":"10.3390\/encyclopedia5020067"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1109\/TGRS.2001.934066","article-title":"Introduction to the special issue on analysis of hyperspectral image data","volume":"39","author":"Landgrebe","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1186\/s13007-024-01273-5","article-title":"Hyperspectral imaging for pest symptom detection in bell pepper","volume":"20","author":"Zemanek","year":"2024","journal-title":"Plant Methods"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1046\/j.1440-6055.1999.00114.x","article-title":"Rapid estimation of numbers of whiteflies (Hemiptera: Aleurodidae) and thrips (Thysanoptera: Thripidae) on sticky traps","volume":"38","author":"Steiner","year":"1999","journal-title":"Aust. J. Entomol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.patcog.2018.11.004","article-title":"High-quality spectral-spatial reconstruction using saliency detection and deep feature enhancement","volume":"88","author":"Xie","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.1109\/TGRS.2009.2016214","article-title":"Spectral\u2013spatial classification of hyperspectral imagery based on partitional clustering techniques","volume":"47","author":"Tarabalka","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1109\/LGRS.2009.2012443","article-title":"Improving hyperspectral image classification using spatial preprocessing","volume":"6","author":"Manian","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, X., Li, R., Wang, M., Liu, Y., Zhang, B., and Zhou, J. (2018). Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables. Hyperspectral Imaging in Agriculture, Food and Environment, IntechOpen.","DOI":"10.5772\/intechopen.72250"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, L., Wu, Y., Yang, N., and Sun, Z. (2025). Advances in Hyperspectral and Diffraction Imaging for Agricultural Applications. Agriculture, 15.","DOI":"10.3390\/agriculture15161775"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"10019","DOI":"10.1021\/acs.jafc.4c11492","article-title":"Hyperspectral Imaging and Deep Learning for Quality and Safety Inspection of Fruits and Vegetables: A Review","volume":"73","author":"Yang","year":"2025","journal-title":"J. Agric. Food Chem."},{"key":"ref_12","unstructured":"Chang, C.-I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1682","DOI":"10.1093\/jee\/99.5.1682","article-title":"Hyperspectral Spectrometry as a Means to Differentiate Uninfested and Infested Winter Wheat by Greenbug (Hemiptera: Aphididae)","volume":"99","author":"Mirik","year":"2006","journal-title":"J. Econ. Entomol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1007\/s10661-023-11678-9","article-title":"UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN","volume":"195","author":"Pansy","year":"2023","journal-title":"Environ. Monit. Assess."},{"key":"ref_15","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":"2018","journal-title":"J. Plant Dis. Prot."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, G., Liu, J., Zhao, C., Li, Z., Huang, Y., Yu, H., Xu, B., Yang, X., Zhu, D., and Zhang, X. (2017). Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives. Front. Plant Sci., 8.","DOI":"10.3389\/fpls.2017.01111"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.crfs.2021.01.002","article-title":"Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review","volume":"4","author":"Saha","year":"2021","journal-title":"Curr. Res. Food Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Vignati, S., Tugnolo, A., Giovenzana, V., Pampuri, A., Casson, A., Guidetti, R., and Beghi, R. (2023). Hyperspectral imaging for fresh-cut fruit and vegetable quality assessment: Basic concepts and applications. Appl. Sci., 13.","DOI":"10.3390\/app13179740"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1007\/s10462-024-11090-w","article-title":"A comprehensive review of deep learning-based hyperspectral image reconstruction for agri-food quality appraisal","volume":"58","author":"Ahmed","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3717","DOI":"10.1111\/1541-4337.12983","article-title":"Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants","volume":"21","author":"Soni","year":"2022","journal-title":"Compr. Rev. Food Sci. Food Saf."},{"key":"ref_21","first-page":"18","article-title":"Detection of disease and pest of kenaf plant using convolutional neural network","volume":"6","author":"Fajri","year":"2021","journal-title":"J. Inf. Technol. Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"012079","DOI":"10.1088\/1742-6596\/1921\/1\/012079","article-title":"Back-Propagation Neural Network (BP-NN) model for the detection of borer pest attack","volume":"1921","author":"Prabhu","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_23","first-page":"635","article-title":"Design and development of a stationary pest infestation monitoring device for rice insect pests using convolutional neural network and raspberry pi","volume":"7","author":"Cubillas","year":"2020","journal-title":"J. Crit. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.2174\/2666255813666191218112436","article-title":"Research on edge detection of agricultural pest and disease leaf image based on LvQ neural network","volume":"14","author":"Fan","year":"2021","journal-title":"Recent Adv. Comput. Sci. Commun."},{"key":"ref_25","first-page":"433","article-title":"Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning","volume":"15","author":"Pattnaik","year":"2021","journal-title":"Intell. Decis. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"80941","DOI":"10.1007\/s11042-024-18562-9","article-title":"An extensive review of hyperspectral image classification and prediction: Techniques and challenges","volume":"83","author":"Tejasree","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e13962","DOI":"10.1016\/j.heliyon.2023.e13962","article-title":"A comparative study revealed hyperspectral imaging as a potential standardized tool for the analysis of cuticle tanning over insect development","volume":"9","author":"Lacotte","year":"2023","journal-title":"Heliyon"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1146\/annurev-ento-010715-023834","article-title":"Remote Sensing and Reflectance Profiling in Entomology","volume":"61","author":"Nansen","year":"2016","journal-title":"Annu. Rev. Entomol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"14118","DOI":"10.1109\/ACCESS.2018.2812999","article-title":"Modern trends in hyperspectral image analysis: A review","volume":"6","author":"Khan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"180750","DOI":"10.1109\/ACCESS.2020.3024891","article-title":"An AIoT based smart agricultural system for pests detection","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","first-page":"138","article-title":"A review on computer vision-based techniques for pest detection and monitoring in agriculture","volume":"7","author":"Singh","year":"2021","journal-title":"J. Imaging"},{"key":"ref_32","first-page":"2094","article-title":"Hyperspectral imaging for insect detection and classification: A review on kernel-based methods","volume":"9","author":"Liu","year":"2019","journal-title":"Appl. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Peignier, S., Lacotte, V., Duport, M.-G., Baa-Puyoulet, P., Simon, J.-C., Calevro, F., Heddi, A., and da Silva, P. (2023). Detection of aphids on hyperspectral images using one-class svm and laplacian of gaussians. Remote Sens., 15.","DOI":"10.3390\/rs15082103"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"295","DOI":"10.3103\/S1060992X23040112","article-title":"Review on Pest Detection and Classification in Agricultural Environments Using Image-Based Deep Learning Models and Its Challenges","volume":"32","author":"Venkatasaichandrakanth","year":"2023","journal-title":"Opt. Mem. Neural Netw."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yamada, M., Thiesen, L.V., Iost Filho, F.H., and Yamamoto, P.T. (2024). Hyperspectral imaging and machine learning: A promising tool for the early detection of Tetranychus urticae Koch infestation in cotton agriculture. Agriculture, 14.","DOI":"10.3390\/agriculture14091573"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2013.07.004","article-title":"Hyperspectral indices for assessing damage by the solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton","volume":"97","author":"Prabhakar","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1111\/zoj.12367","article-title":"Integrative insect taxonomy based on morphology, mitochondrial DNA, and hyperspectral reflectance profiling","volume":"177","author":"Wang","year":"2015","journal-title":"Zool. J. Linn. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104751","DOI":"10.1016\/j.chemolab.2023.104751","article-title":"Evaluation of the potential of near-infrared hyperspectral imaging for monitoring the invasive brown marmorated stink bug","volume":"234","author":"Ferrari","year":"2023","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tavares, G.C., and Silva, N.C. (2024). Images in red: A methodological and integrative approach for the usage of Near-infrared Hyperspectral Imaging (NIR-HSI) on collection specimens of Orthoptera (Insecta). bioXiv.","DOI":"10.1101\/2024.10.12.617997"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s00340-016-6485-x","article-title":"Optical characterization of agricultural pest insects: A methodological study in the spectral and time domains","volume":"122","author":"Li","year":"2016","journal-title":"Appl. Phys. B"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"112420","DOI":"10.1016\/j.optlastec.2025.112420","article-title":"Visible-NIR Hyperspectral Imaging Based on Characteristic Spectral Distillation Used for Species Identification of Similar Crickets","volume":"183","author":"Ma","year":"2025","journal-title":"Opt. Laser Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"14858","DOI":"10.1364\/OE.557318","article-title":"Polarization resolved hyperspectral imaging of the beetle Protaetia speciosa jousselini","volume":"33","author":"Soltani","year":"2025","journal-title":"Opt. Express"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"32","DOI":"10.47852\/bonviewJOPR32021587","article-title":"Role of Micro-Architectures on Insects\u2019 Elytra Affects the Nanomechanical and Optical Properties: Inspired for Designing the Lightweight Materials","volume":"1","author":"Jyoti","year":"2024","journal-title":"J. Opt. Photonics Res."},{"key":"ref_44","first-page":"183","article-title":"Near-infrared (NIR)-reflectance in insects\u2013Phenetic studies of 181 species","volume":"24","author":"Mielewczik","year":"2012","journal-title":"Entomol. Heute"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Huang, L., Yang, L., Meng, L., Wang, J., Li, S., Fu, X., Du, X., and Wu, D. (2018). Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves. Sensors, 18.","DOI":"10.3390\/s18072077"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s44187-025-00338-1","article-title":"Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms","volume":"5","author":"Ajith","year":"2025","journal-title":"Discov. Food"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"42277","DOI":"10.1007\/s11042-023-15221-3","article-title":"Survey on crop pest detection using deep learning and machine learning approaches","volume":"82","author":"Chithambarathanu","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Orlando, V.S.W., Galo, M.d.L.B.T., Martins, G.D., Lingua, A.M., de Assis, G.A., and Belcore, E. (2024). Hyperspectral Characterization of Coffee Leaf Miner (Leucoptera coffeella) (Lepidoptera: Lyonetiidae) Infestation Levels: A Detailed Analysis. Agriculture, 14.","DOI":"10.3390\/agriculture14122173"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Feng, S., Jiang, S., Huang, X., Zhang, L., Gan, Y., Wang, L., and Zhou, C. (2024). Detection of Rice Leaf Folder in Paddy Fields Based on Unmanned Aerial Vehicle-Based Hyperspectral Images. Agronomy, 14.","DOI":"10.3390\/agronomy14112660"},{"key":"ref_50","unstructured":"Moskvichev, A.Y. (2015). Phytosanitary Control of Plants: A Textbook, Volgograd State Agricultural University."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Medvedev, G.S. (1988). Keys to the Insects of the European Part of the USSR, U.S. Department of Commerce.","DOI":"10.1163\/9789004628007"},{"key":"ref_52","first-page":"55","article-title":"Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 \u03bcm","volume":"43","author":"Kokaly","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"6039","DOI":"10.1007\/s11694-023-02044-x","article-title":"Wavelength and texture feature selection for hyperspectral imaging: A systematic literature review","volume":"17","author":"Rogers","year":"2023","journal-title":"J. Food Meas. Charact."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1093\/biolinnean\/blac064","article-title":"A generalized approach to characterize optical properties of natural objects","volume":"137","author":"Roberts","year":"2022","journal-title":"Biol. J. Linn. Soc."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1088\/0026-1394\/48\/3\/010","article-title":"Bandwidth correction for generalized bandpass functions","volume":"48","author":"Woolliams","year":"2011","journal-title":"Metrologia"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1111\/j.1467-9469.2005.00426.x","article-title":"The skew-normal distribution and related multivariate families","volume":"32","author":"Azzalini","year":"2005","journal-title":"Scand. J. Stat."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1093\/biomet\/78.3.691","article-title":"A note on a general definition of the coefficient of determination","volume":"78","author":"Nagelkerke","year":"1991","journal-title":"Biometrika"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1111\/j.1469-185X.1973.tb01570.x","article-title":"The chitin system","volume":"48","author":"Rudall","year":"1973","journal-title":"Biol. Rev."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.ijbiomac.2019.12.194","article-title":"Extraction, characterization and comparison of chitins from large bodied four Coleoptera and Orthoptera species","volume":"145","author":"Kabalak","year":"2020","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1016\/j.ijbiomac.2014.09.034","article-title":"Comparison of chitin structures isolated from seven different insects","volume":"72","author":"Kaya","year":"2015","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s00359-002-0307-9","article-title":"Colour in the eyes of insects","volume":"188","author":"Stavenga","year":"2002","journal-title":"J. Comp. Physiol. A"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1111\/j.1744-7917.2006.00077.x","article-title":"Morphological structure and optical properties of the wings of Morphidae","volume":"13","author":"Berthier","year":"2006","journal-title":"Insect Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"105659","DOI":"10.1016\/j.dib.2020.105659","article-title":"Why orthoptera fauna resist of pesticide? First experimental data of resistance phenomena","volume":"30","author":"Brahimi","year":"2020","journal-title":"Data Brief"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Che\u2019Ya, N.N., Mohidem, N.A., Roslin, N.A., Saberioon, M., Tarmidi, M.Z., Arif Shah, J., Fazlil Ilahi, W.F., and Man, N. (2022). Mobile Computing for Pest and Disease Management Using Spectral Signature Analysis: A Review. Agronomy, 12.","DOI":"10.3390\/agronomy12040967"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1007\/s11760-021-02029-7","article-title":"Pest identification via hyperspectral image and deep learning","volume":"16","author":"Xiao","year":"2022","journal-title":"SIViP"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.patcog.2016.10.019","article-title":"Hyperspectral image reconstruction by deep convolutional neural network for classification","volume":"63","author":"Li","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral-spatial residual network for hyperspectral image classification: A 3-d deep learning framework","volume":"56","author":"Zhong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-7737\/14\/12\/1715\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T13:38:11Z","timestamp":1764596291000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-7737\/14\/12\/1715"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,1]]},"references-count":67,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["biology14121715"],"URL":"https:\/\/doi.org\/10.3390\/biology14121715","relation":{},"ISSN":["2079-7737"],"issn-type":[{"value":"2079-7737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,1]]}}}