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1 Thomas, S.; Kuska, M.T.; Bohnenkamp, D.; Brugger, A.; Alisaac, E.; Wahabzada, M.; et al. Benefits of Hyperspectral Imaging for Plant Disease Detection and Plant Protection: A Technical Perspect ive. J. Plant Dis. Prot. 2018, 125, 5–20. https://doi.org/10.1007/s41348-017-0124-6

2 García-Vera, Y.E.; Polochè-Arango, A.; Mendivelso-Fajardo, C.A.; Gutiérrez-Bernal, F.J. Hyperspectral Image Analysis and Machine Learning Techniques for Crop Disease Detection and Identification: A Review. Sustainability 2024, 16, 6064. https://doi.org/10.3390/su16146064

3 Lu, B.; Dao, P.D.; Liu, J.; He, Y.; Shang, J. Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens. 2020, 12, 2659. https://doi.org/10.3390/rs12162659

4 Terentev, A.; Dolzhenko, V.; Fedotov, A.; Eremenko, D. Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review. Sensors 2022, 22, 757. https://doi.org/10.3390/s22030757

5 Wan, L.; Li, H.; Li, C.; Wang, A.; Yang, Y.; Wang, P. Hyperspectral sensing of plant diseases: Principle and methods. Agronomy 2022, 12, 1451. https://doi.org/10.3390/agronomy12061451

6 Rogers, M.; Blanc-Talon, J.; Urschler, M.; Delmas, P. Wavelength and Texture Feature Selection for Hyperspectral Imaging: A Systematic Literature Review. Journal of Food Measurement and Characterization 2023, 17, 6039–6064. https://doi.org/10.1007/s11694-023-02044-x

7 Chen, L.; Wu, Y.; Yang, N.; Sun, Z. Advances in Hyperspectral and Diffraction Imaging for Agricultural Applications. Agriculture 2025, 15, 1775. https://doi.org/10.3390/agriculture15161775

8 Carter, G.A.; Knapp, A.K. Leaf Optical Properties in Higher Plants: Linking Spectral Characteristics to Stress and Chlorophyll Concentration. Am. J. Bot. 2001, 88, 677–684. https://doi.org/10.2307/2657068

9 Zhang, H.; Huang, L.; Huang, W.; Dong, Y.; Weng, S.; Zhao, J.; Ma, H.; Liu, L. Detection of Wheat Fusarium Head Blight Using UAV-Based Spectral and Image Feature Fusion. Front. Plant Sci. 2022, 13, 1004427. https://doi.org/10.3389/fpls.2022.1004427

10 Kaur, S.; Kakani, V.G.; Carver, B.; Jarquin, D.; Singh, A. Hyperspectral Imaging Combined with Machine Learning for High-Throughput Phenotyping in Winter Wheat. Plant Phenome J. 2024, 7, e20111. https://doi.org/10.1002/ppj2.20111

11 Deng, J.; Zhang, X.; Yang, Z.; Zhou, C.; Wang, R.; Zhang, K.; Lv, X.; Yang, L.; Wang, Z.; Li, P.; et al. Pixel-Level Regression for UAV Hyperspectral Images: Deep Learning-Based Quantitative Inverse of Wheat Stripe Rust Disease Index. Comput. Electron. Agric. 2023, 215, 108434. https://doi.org/10.1016/j.compag.2023.108434

12 Abdulridha, J.; Min, A.; Rouse, M.N.; Kianian, S.; Isler, V.; Yang, C. Evaluation of Stem Rust Disease in Wheat Fields by Drone Hyperspectral Imaging. Sensors 2023, 23, 4154. https://doi.org/10.3390/s23084154

13 Okyere, F.G.; et al. Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions in Wheat. Remote Sens. 2024, 16, 3446. https://doi.org/10.3390/rs16183446

14 Shahin, M.A.; Symons, S.J. Detection of Fusarium Damaged Kernels in Canada Western Red Spring Wheat Using Visible/Near-Infrared Hyperspectral Imaging and Principal Component Analysis. Comput. Electron. Agric. 2011, 75, 107–112. https://doi.org/10.1016/j.compag.2010.10.004

15 Povolotckaia, A.; Pankin, D.; Gareev, M.; Serebrjakov, D.; Gulyaev, A.; Borisov, E.; Boyko, A.; Borzenko, S.; Belousov, S.; Noy, O.; Moskovskiy, M. Hyperspectral imaging study of wheat grains infected with several Fusarium fungal species and their identification with PCA-based approach. Molecules 2025, 30, 2586. https://doi.org/10.3390/molecules30122586

16 Niu, Z.; Li, Y.; Moncada, J.D.S.; Johnson, W.; Lang, E.B.; Li, X.; Jin, J. Proximal Hyperspectral Imaging for Early Detection and Disease Development Prediction of Septoria Leaf Blotch in Wheat Using Spectral–Temporal Features. Comput. Electron. Agric. 2025, 235, 110400. https://doi.org/10.1016/j.compag.2025.110400

17 Feng, Z.-H.; Wang, L.-Y.; Yang, Z.-Q.; Zhang, Y.-Y.; Li, X.; Song, L.; He, L.; Duan, J.-Z.; Feng, W. Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning. Front. Plant Sci. 2022, 13, 828454. https://doi.org/10.3389/fpls.2022.828454

18 Chossegros, M.; Hubbard, A.; Burt, M.; Harrison, R.J.; Nellist, C.F.; Grinberg, N.F. Hyperspectral image analysis for classification of multiple infections in wheat. Plant Methods 2025, 21, 144. https://doi.org/10.1186/s13007-025-01461-x

19 Grieco, M.; Schmidt, M.; Warnemünde, S. Dynamics and genetic regulation of leaf nutrient concentration in barley based on hyperspectral imaging and machine learning. Plant Sci. 2022, 315, 111123. https://doi.org/10.1016/j.plantsci.2021.111123

20 Busov, I.D.; Genaev, M.A.; Komyshev, E.G.; Koval, V.S.; Zykova, T.E.; Glagoleva, A.Y.; Afonnikov, D.A. A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an example. Vavilovskii Zhurnal Genetiki i Selektsii 2024, 28, 443–455. https://doi.org/10.18699/vjgb-24-50

21 Fan, K.-J.; Liu, B.-Y.; Su, W.-H. Discrimination of deoxynivalenol levels of barley kernels using hyperspectral imaging in tandem with optimized convolutional neural network. Sensors 2023, 23, 2668. https://doi.org/10.3390/s23052668

22 Yu, K.; Lenz-Wiedemann, V.; Chen, X.; Bareth, G. Estimating Leaf Chlorophyll of Barley at Different Growth Stages Using Spectral Indices to Reduce Soil Background and Canopy Structure Effects. ISPRS Journal of Photogrammetry and Remote Sensing 2014, 97, 58–77. https://doi.org/10.1016/j.isprsjprs.2014.08.005

23 Ganeva, D.; Roumenina, E.; Dimitrov, P.; Gikov, A.; Jelev, G.; Dyulgenova, B.; Valcheva, D.; Bozhanova, V. Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs. Sensors 2023, 23, 5008. https://doi.org/10.3390/s23115008

24 Ashourloo, D.; Nematollahi, H.; Huete, A.; Aghighi, H.; Azadbakht, M.; Salehi Shahrabi, H.; Goodarzdashti, S. A New Phenology-Based Method for Mapping Wheat and Barley Using Time-Series of Sentinel-2 Images. Remote Sensing of Environment 2022, 280, 113206. https://doi.org/10.1016/j.rse.2022.113206

25 Thomas, S.; Behmann, J.; Steier, A.; et al. Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform. Plant Methods 2018, 14, 45. https://doi.org/10.1186/s13007-018-0313-8

26 Rezaei, M.; Gupta, S.; Diepeveen, D.; Laga, H.; Jones, M.G.K.; Sohel, F. Barley disease recognition using deep neural networks. Eur. J. Agron. 2024, 161, 127359. https://doi.org/10.1016/j.eja.2024.127359

27 Terentev, A.; Kuznetsova, D.; Fedotov, A.; Baranova, O.; Eremenko, D. Cross-crop transferability of machine learning models for early stem rust detection in wheat and barley using hyperspectral imaging. Plants 2025, 14, 3265. https://doi.org/10.3390/plants14213265

28 Поляков, И.Я.; Персов, М.П.; Смирнов, В.А. Прогноз развития вредителей и болезней сельскохозяйственных культур (с практикумом); Колос: Ленинград, 1984; 318 с.

29 ГОСТ 12044-93. Семена сельскохозяйственных культур. Методы определения зараженности болезнями; Стандартинформ: Москва, 2011; с. 57.

30 Kokaly, R.F.; Skidmore, A.K. Plant Phenolics and Absorption Features in Vegetation Reflectance Spectra Near 1.66 μm. Int. J. Appl. Earth Obs. Geoinf. 2015, 43, 55–83. https://doi.org/10.1016/j.jag.2015.01.010

31 Spectral Imaging for Remote Sensing. Available online: https://gisproxima.ru/spektralnaya_semka_dlya_dzz (accessed on 10 October 2025).

32 Merényi, E.; Farrand, W.H.; Taranik, J.V.; et al. Classification of Hyperspectral Imagery with Neural Networks: Comparison to Conventional Tools. EURASIP J. Adv. Signal Process. 2014, 2014, 71. https://doi.org/10.1186/1687-6180-2014-71

33 Peignier, S.; Lacotte, V.; Duport, M.-G.; Baa-Puyoulet, P.; Simon, J.-C.; Calevro, F.; Heddi, A.; da Silva, P. Detection of Aphids on Hyperspectral Images Using One-Class SVM and Laplacian of Gaussians. Remote Sens. 2023, 15, 2103. https://doi.org/10.3390/rs15082103

34 Xia, J.; Falco, N.; Benediktsson, J.A.; Du, P.; Chanussot, J. Hyperspectral Image Classification With Rotation Random Forest Via KPCA. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 10, 1601–1609. https://doi.org/10.1109/JSTARS.2016.2636877

35 Wang, Y.; Ma, H.; Yang, Y.; Zhao, E.; Song, M.; Yu, C. Self-Supervised Deep Multi-Level Representation Learning Fusion-Based Maximum Entropy Subspace Clustering for Hyperspectral Band Selection. Remote Sens. 2024, 16, 224. https://doi.org/10.3390/rs16020224

36 Shi, G.; Zhang, X.; Qu, G.; Chen, Z. Classification of Rice Varieties Using SIMCA Applied to NIR Spectroscopic Data. ACS Omega 2022, 7, 46623–46628. https://doi.org/10.1021/acsomega.2c05561

37 Opitz, J. A Closer look at classification evaluation metrics and a critical reflection of common evaluation practice. Trans. Assoc. Comput. Linguist. 2024, 12, 820–836. https://doi.org/10.1162/tacl_a_00675

38 Graham, C.; Girkin, J.; Bourgenot, C. Spectral Index Selection Method for Remote Moisture Sensing under Challenging Illumination Conditions. Scientific Reports 2022, 12, 14555. https://doi.org/10.1038/s41598-022-18801-9

39 Fite, N. B., Wegari, G. M., & Steendam, H. (2025). Integration of Artificial Neural Network Regression and Principal Component Analysis for Indoor Visible Light Positioning. Sensors 2025, 25(4), 1049. https://doi.org/10.3390/s25041049

40 Ospina-Rozo, L.; Roberts, A.; Stuart-Fox, D. A generalized approach to characterize optical properties of natural objects. Biol. J. Linn. Soc. 2022, 137, 534–555. https://doi.org/10.1093/biolinnean/blac064

41 Woolliams, E.; Baribeau, R.; Bialek, A.; Cox, M. Bandwidth correction for generalized bandpass functions. Metrologia 2011, 48, 164. https://doi.org/10.1088/0026-1394/48/3/010

42 Azzalini, A. The skew-normal distribution and related multivariate families. Scand. J. Stat. 2005, 32, 159–188. https://doi.org/10.1111/j.1467-9469.2005.00426.x

43 Kozak, J., Probierz, B., Kania, K., & Juszczuk, P. (2022). Preference-Driven Classification Measure. Entropy, 24(4), 531. https://doi.org/10.3390/e24040531

44 Park, E.; Kim, Y.-S.; Faqeerzada, M.A.; Kim, M.S.; Baek, I.; Cho, B.-K. Hyperspectral Reflectance Imaging for Nondestructive Evaluation of Root Rot in Korean Ginseng (Panax ginseng Meyer). Front. Plant Sci. 2023, 14, 1109060. https://doi.org/10.3389/fpls.2023.1109060

45 Chattopadhyay, N.; Shukla, K.K.; Birah, A.; et al. Identification of Spectral Bands to Discriminate Wheat Spot Blotch Using in Situ Hyperspectral Data. J. Indian Soc. Remote Sens. 2023, 51, 917–934. https://doi.org/10.1007/s12524-023-01673-5

46 Terentev, A.; Badenko, V.; Shaydayuk, E.; Emelyanov, D.; Eremenko, D.; Klabukov, D.; Fedotov, A.; Dolzhenko, V. Hyperspectral Remote Sensing for Early Detection of Wheat Leaf Rust Caused by Puccinia triticina. Agriculture 2023, 13, 1186. https://doi.org/10.3390/agriculture13061186

47 Feng, G.; Gu, Y.; Wang, C.; Zhou, Y.; Huang, S.; Luo, B. Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective. Plants 2024, 13, 1722. https://doi.org/10.3390/plants13131722

48 Yu, K.; Anderegg, J.; Mikaberidze, A.; Karisto, P.; Mascher, F.; McDonald, B.A.; Walter, A.; Hund, A. Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease. Front. Plant Sci. 2018, 9, 1195. https://doi.org/10.3389/fpls.2018.01195

49 Bauriegel, E.; Giebel, A.; Herppich, W.B. Hyperspectral and Chlorophyll Fluorescence Imaging to Analyse the Impact of Fusarium culmorum on the Photosynthetic Integrity of Infected Wheat Ears. Sensors 2011, 11, 3765–3779. https://doi.org/10.3390/s110403765

50 Zhang, P.; Chen, Y.; Zhang, Y.; Xiang, J.; Wang, Y.; Wang, Z.; Chen, H.; Zhang, Y. Chloroplast Damage and Photosynthetic System Disorder Induced Chlorosis in the Leaves of Rice Seedlings under Excessive Biuret. Agronomy 2023, 13, 2052. https://doi.org/10.3390/agronomy13082052

51 Lujan, P.; Sanogo, S.; Puppala, N.; Randall, J. Factors Affecting Mycelium Pigmentation and Pathogenicity of Sclerotinia sclerotiorum on Valencia Peanut. Can. J. Plant Sci. 2016, 96, 461–473. https://doi.org/10.1139/cjps-2015-0258

52 Netherway, T.; Bengtsson, J.; Buegger, F.; et al. Pervasive Associations Between Dark Septate Endophytic Fungi with Tree Root and Soil Microbiomes Across Europe. Nat. Commun. 2024, 15, 159. https://doi.org/10.1038/s41467-023-44172-4

53 Gautier, V.; Levert, E.; Giraud, T.; Silar, P. Important Role of Melanin for Fertility in the Fungus Podospora anserina. G3 Genes Genomes Genet. 2021, 11, jkab159. https://doi.org/10.1093/g3journal/jkab159

54 Yu, X.; Huo, L.; Liu, H.; Chen, L.; Wang, Y.; Zhu, X. Melanin is Required for the Formation of the Multi-Cellular Conidia in the Endophytic Fungus Pestalotiopsis microspora. Microbiol. Res. 2015, 179, 1–11. https://doi.org/10.1016/j.micres.2015.06.004

55 Reyes-Fernández, E.Z.; Shi, Y.; Grün, P.; Bode, H.B.; Bölker, M. An Unconventional Melanin Biosynthesis Pathway in Ustilago maydis. Appl. Environ. Microbiol. 2021, 87, e01510-20. https://doi.org/10.1128/AEM.01510-20

56 Tao, S.Q.; Cao, B.; Tian, C.M.; et al. Comparative Transcriptome Analysis and Identification of Candidate Effectors in Two Related Rust Species (Gymnosporangium yamadae and Gymnosporangium asiaticum). BMC Genomics 2017, 18, 651. https://doi.org/10.1186/s12864-017-4059-x

57 Zhan, G.; Guo, J.; Tian, Y.; et al. High-Throughput RNA Sequencing Reveals Differences Between the Transcriptomes of the Five Spore Forms of Puccinia striiformis f. sp. tritici, the Wheat Stripe Rust Pathogen. Stress Biol. 2023, 3, 29. https://doi.org/10.1007/s44154-023-00107-z

58 Aranda, C.; Méndez, I.; Barra, P.J.; Hernández-Montiel, L.; Fallard, A.; Tortella, G.; Briones, E.; Durán, P. Melanin Induction Restores the Pathogenicity of Gaeumannomyces graminis var. tritici in Wheat Plants. J. Fungi 2023, 9, 350. https://doi.org/10.3390/jof9030350

59 Wegulo, S.N. Tan Spot of Cereals. Plant Health Instr. 2011, 11, PHI-I-2011-0426-01. https://doi.org/10.1094/PHI-I-2011-0426-01

60 Bohnenkamp, D.; Behmann, J.; Paulus, S.; Steiner, U.; Mahlein, A.-K. A Hyperspectral Library of Foliar Diseases of Wheat. Phytopathology 2021, 111, 1583–1593. https://doi.org/10.1094/PHYTO-09-19-0335-R

61 Zhao, L.; Su, P.; Hou, B.; Wu, H.; Fan, Y.; Li, W.; Zhao, J.; Ge, W.; Xu, S.; Wu, S.; et al. The Black Necrotic Lesion Enhanced Fusarium graminearum Resistance in Wheat. Front. Plant Sci. 2022, 13, 926621. https://doi.org/10.3389/fpls.2022.926621

62 Schafer, J.F. Rusts, Smuts, and Powdery Mildew. In Wheat and Wheat Improvement, 2nd ed.; Heyne, E.G., Ed.; ASA, CSSA, SSSA: Madison, WI, USA, 1987; pp. 542–584.

63 Studt, L.; Wiemann, P.; Kleigrewe, K.; Humpf, H.; Tudzynski, B. Biosynthesis of Fusarubins Accounts for Pigmentation of Fusarium fujikuroi Perithecia. Appl. Environ. Microbiol. 2012, 78, 1–12. https://doi.org/10.1128/AEM.00823-12

64 Cambaza, E. Comprehensive Description of Fusarium graminearum Pigments and Related Compounds. Foods 2018, 7, 165. https://doi.org/10.3390/foods7100165

65 Wójtowicz, A.; Piekarczyk, J.; Czernecki, B.; Ratajkiewicz, H. A Random Forest Model for the Classification of Wheat and Rye Leaf Rust Symptoms Based on Pure Spectra at Leaf Scale. J. Photochem. Photobiol. 2021, 223, 112278. https://doi.org/10.1016/j.jphotobiol.2021.112278

66 Javidan, S.M.; Banakar, A.; Vakilian, K.A.; Ampatzidis, Y.; Rahnama, K. Early Detection and Spectral Signature Identification of Tomato Fungal Diseases (Alternaria alternata, Alternaria solani, Botrytis cinerea, and Fusarium oxysporum) by RGB and Hyperspectral Image Analysis and Machine Learning. Heliyon 2024, 10, e38017. https://doi.org/10.1016/j.heliyon.2024.e38017

67 Knauer, U.; Matros, A.; Petrovic, T.; et al. Improved Classification Accuracy of Powdery Mildew Infection Levels of Wine Grapes by Spatial-Spectral Analysis of Hyperspectral Images. Plant Methods 2017, 13, 47. https://doi.org/10.1186/s13007-017-0198-y

68 Lagashetti AC, Dufossé L, Singh SK, Singh PN. Fungal Pigments and Their Prospects in Different Industries. Microorganisms. 2019 Nov 22;7(12):604. https://doi.org/10.3390/microorganisms7120604

69 Masi M, Zorrilla JG, Meyer S. Bioactive Metabolite Production in the Genus Pyrenophora (Pleosporaceae, Pleosporales). Toxins (Basel). 2022 Aug 27;14(9):588. https://doi.org/10.3390/toxins14090588

70 Davoli, P.; Weber, R.W. Identification and Quantification of Carotenoid Pigments in Aeciospores of the Daisy Rust Fungus, Puccinia distincta. Phytochemistry 2002, 60, 309–313. https://doi.org/10.1016/s0031-9422(02)00120-6

71 Ualiyeva, R.M.; Kaverina, M.M.; Osipova, A.V.; Kairbayev, Y.B.; Zhangazin, S.B.; Iksat, N.N.; Mapitov, N.B. VNIR Hyperspectral Signatures for Early Detection and Machine-Learning Classification of Wheat Diseases. Plants 2025, 14, 3644. https://doi.org/10.3390/plants14233644