J. For. Sci., X:X | DOI: 10.17221/22/2026-JFS

The role of hyperspectral imaging in forest seedling phenotypingReview

Martina Đodan ORCID...1, Sanja Perić2, Karmen Vugdelija ORCID...1
1 Department for Silviculture, Croatian Forest Research Institute, Jastrebarsko, Croatia
2 Common Affairs Service, Croatian Forest Research Institute, Jastrebarsko, Croatia


In recent years, hyperspectral imaging has been widely adopted in agriculture and plant phenotyping, while its application in forestry has been increasing. From that point onward, hyperspectral imaging has become a valuable tool for plant phenotyping, enabling the assessment of a broad range of plant traits. Given that seedlings of forest trees are one of the most widely used types of forest planting stock, advancements in hyperspectral technology have created new possibilities for improving seedling quality assessment. High-quality forest seedlings are important for the successful establishment of forest stands, especially after outplanting within restoration initiatives. Even though hyperspectral imaging brings numerous advantages, continued technological improvements are necessary to address its several limitations and challenges. Despite its widespread use in agricultural phenotyping, applications in forest nursery production remain limited. Therefore, this review focuses on research involving hyperspectral imaging in forest seedling production and its potential for assessing seedling quality parameters.

Keywords: plant evaluation; remote sensing; research gaps; technological approaches; spectral imaging techniques

Received: March 2, 2026; Revised: May 20, 2026; Accepted: May 25, 2026; Prepublished online: June 22, 2026 

Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Ančić M., Pernar R., Cindrić F.B., Seletković A., Kolić J. (2019): Hiperspektralni senzori i primjena u šumarstvu. Nova mehanizacija šumarstva, 40: 71-78. (in Croatian) Go to original source...
    2. Awada L., Phillips P.W.B., Bodan A.M. (2024): The evolution of plant phenomics: Global insights, trends, and collaborations (2000-2021). Frontiers in Plant Science, 15: 1410738. Go to original source...
    3. Babić L., Miljković V., Odak I., Đapo A. (2023): Hyperspectral imaging in preservation of Croatia's historic trees: A case study of Dedek Oak in Maksimir Park. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII 1/W2 - ISPRS Geospatial Week 2023, Cairo, Sept 2-7, 2023: 1853-1859. Go to original source...
    4. Ballanti L., Blesius L., Hines E., Kruse B. (2016): Tree species classification using hyperspectral imagery: A comparison of two classifiers. Remote Sensing, 8: 445. Go to original source...
    5. Banerjee B.P., Joshi S., Thoday-Kennedy E., Pasam R.K., Tibbits J., Hayden M., Spangenberg G., Kant S. (2020): High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response. Journal of Experimental Botany, 71: 4604-4615. Go to original source... Go to PubMed...
    6. Behmann J., Mahlein A.K., Paulus S., Dupuis J., Kuhlmann H., Oerke E.C., Plümer L. (2016): Generation and application of hyperspectral 3D plant models: Methods and challenges. Machine Vision and Applications, 27: 611-624. Go to original source...
    7. Behmann J., Acebron K., Emin D., Bennertz S., Matsubara S., Thomas S., Bohnenkamp D., Kuska M.T., Jussila J., Salo H., Mahlein A.K., Rascher U. (2018): Specim IQ: Evaluation of a new, miniaturized handheld hyperspectral camera and its application for plant phenotyping and disease detection. Sensors, 18: 441. Go to original source...
    8. Bian L., Zhang H., Ge Y., Čepl J., Stejskal J., El-Kassaby Y.A. (2022): Closing the gap between phenotyping and genotyping: Review of advanced, image-based phenotyping technologies in forestry. Annals of Forest Science, 79: 22. Go to original source...
    9. Chang C.I. (2013): Hyperspectral Data Processing: Algorithm Design and Analysis. Hoboken, John Wiley & Sons: 512. Go to original source...
    10. Čirjak D., Miklečić I., Lemić D., Kos T., Pajač Živković I. (2022): The use of RGB and hyperspectral imaging in detection of codling moth and its damages on apple. In: Proceedings of the XIII International Scientific Agricultural Symposium Agrosym 2022, Sarajevo, Oct 6-9, 2022: 696-701.
    11. Cotrozzi L. (2022): Spectroscopic detection of forest diseases: A review (1970-2020). Journal of Forestry Research, 33: 21-38. Go to original source...
    12. Dalponte M., Bruzzone L., Gianelle D. (2009): Fusion of hyperspectral and lidar remote sensing data for the estimation of tree stem diameters. In: Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, July 13-17, 2009: 1008-1011. Go to original source...
    13. Detring J., Barreto A., Mahlein A.K., Paulus S. (2024): Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping. Plant Methods, 20: 189. Go to original source...
    14. Dmitriev P.A., Kozlovsky B.L., Dmitrieva A.A. (2024): Vegetation and dormancy states identification in coniferous plants based on hyperspectral imaging data. Horticulturae, 10: 241. Go to original source...
    15. Đodan M., Barišić A. (2026): Kvaliteta šumskog sadnog materijala - važnost, opis i metodologija. Šumarski list, 150: 47-54. (in Croatian) Go to original source...
    16. Đodan M., Bogdanić R., Gradečki Poštenjak M., Perić S. (2023): Production of broadleaves forest planting material in the Republic of Croatia from 2017 to 2021. Šumarski list, 147: 555-564. Go to original source...
    17. Faehn C., Konert G., Keinänen M., Karppinen K., Krause K. (2024): Advancing hyperspectral imaging techniques for root systems: A new pipeline for macro and microscale image acquisition and classification. Plant Methods, 20: 171. Go to original source...
    18. Fahlgren N., Gehan M.A., Baxter I. (2015): Lights, camera, action: High-throughput plant phenotyping is ready for a close-up. Current Opinion in Plant Biology, 24: 93-99. Go to original source... Go to PubMed...
    19. Fassnacht F.E., Latifi H., Stereńczak K., Modzelewska A., Lefsky M., Waser L.T., Straub C., Ghosh A. (2016): Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 186: 64-87. Go to original source...
    20. Felix M.J.B., Main R., Watt M.S., Arpanaei M.M., Patuawa T. (2025): Early detection of water stress in Kauri seedlings using multitemporal hyperspectral indices and inverted plant traits. Remote Sensing, 17: 463. Go to original source...
    21. Goetz A.F.H. (2009): Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sensing of Environment, 113: 5-16. Go to original source...
    22. Goetz A.F.H., Vane G., Solomon J.E., Rock B.N. (1985): Imaging spectrometry for Earth remote sensing. Science, 228: 1147-1153. Go to original source... Go to PubMed...
    23. Grossnickle S.C. (2012): Why seedlings survive: Influence of plant attributes. New Forests, 43: 711-738. Go to original source...
    24. Grossnickle S.C. (2018): Seedling establishment on a forest restoration site: An ecophysiological perspective. Reforesta, 6: 110-139. Go to original source...
    25. Grossnickle S.C., MacDonald J.E. (2018): Seedling quality: History, application, and plant attributes. Forests, 9: 283. Go to original source...
    26. Haase D.L. (2007): Morphological and physiological evaluations of seedling quality. In: Riley L.E., Dumroese R.K., Landis T.D. (eds): National Proceedings: Forest and Conservation Nursery Associations - 2006, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, June 19-22, 2006: 3-8.
    27. Haase D.L. (2008): Understanding forest seedling quality: Measurements and interpretation. Tree Planters' Notes, 52: 24-30.
    28. Haase D.L. (2010): Strategies and challenges for nursery production: Perspectives on where we're going and where we've been. In: Riley L.E., Pinto J.R., Dumroese R.K. (eds): National Proceedings: Forest and Conservation Nursery Associations - 2009, Fort Collins, Department of Agriculture, Forest Service, Rocky Mountain Research Station, July 14-23, 2009: 38-44.
    29. Huang Z., Zhu T., Li Z., Ni C. (2021): Non-destructive testing of moisture and nitrogen content in Pinus massoniana seedling leaves with NIRS based on MS-SC-CNN. Applied Sciences, 11: 2754. Go to original source...
    30. Huber S., Kneubühler M., Psomas A., Itten K., Zimmermann N.E. (2008): Estimating foliar biochemistry from hyperspectral data in mixed forest canopy. Forest Ecology and Management, 256: 491-501. Go to original source...
    31. Jaenicke H. (1999): Good Tree Nursery Practices: Practical Guidelines for Research Nurseries. Nairobi, International Centre for Research in Agroforestry (ICRAF): 93.
    32. Jones A., Gardner A., Hayes F., Pfrang C., Jeffers E.S. (2025): The effect of elevated CO2 on hyperspectral leaf reflectance in mature trees. Trees, 39: 69. Go to original source... Go to PubMed...
    33. Kim Y., Glenn D.M., Park J., Ngugi H.K., Lehman B.L. (2011): Hyperspectral image analysis for water stress detection of apple trees. Computers and Electronics in Agriculture, 77: 155-160. Go to original source...
    34. Liu X., Li N., Huang Y., Lin X., Ren Z. (2023): A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology. Frontiers in Plant Science, 13: 1084847. Go to original source...
    35. Long T., Che X., Guo W., Lan Y., Xie Z., Liu W., Lv J., Long Y., Liu T., Zhao J. (2023): Visible-near-infrared hyperspectral imaging combined with ensemble learning for the nutrient content of Pinus elliottii × P. caribaea canopy needles detection. Frontiers in Forests and Global Change, 6: 1203626. Go to original source...
    36. Lu Y., Payn K.G., Pandey P., Acosta J.J., Heine A.J., Walker T.D., Young S. (2021): Hyperspectral imaging with cost-sensitive learning for high-throughput screening of loblolly pine (Pinus taeda L.) seedlings for freeze tolerance. Transactions of the ASABE, 64: 2045-2059. Go to original source...
    37. Lucas R., Rowlands A., Niemann O., Merton R. (2004): Hyperspectral sensors and applications. In: Varshney P.K., Arora M.K. (eds): Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data. Berlin, Springer-Verlag: 11-49. Go to original source...
    38. Luo B., Sun H., Zhang L., Chen F., Wu K. (2024): Advances in the tea plants phenotyping using hyperspectral imaging technology. Frontiers in Plant Science, 15: 1442225. Go to original source...
    39. Mahlein A.K., Kuska M.T., Behmann J., Polder G., Walter A. (2018): Hyperspectral sensors and imaging technologies in phytopathology: State of the art. Annual Review of Phytopathology, 56: 535-558. Go to original source... Go to PubMed...
    40. Mataruga M., Cvjetković B., De Cuyper B., Aneva I., Zhelev P., Cudlín P., Metslaid M., Kankaanhuhta V., Collet C., Annighöfer P., Mathes T., Marianthi T., Despoina P., Jónsdóttir R.J., Monteverdi M.C., de Dato G., Mariotti B., Kolevska D.D., Lazarević J., Fløistad I.S., Klisz M., Gil W., Paiva V., Fonseca T., Nicolescu V.N., Popović V., Devetaković J., Repáč I., Božič G., Kraigher H., Andivia E., Diez J.J., Böhlenius H., Löf M., Bilir N., Villar-Salvador P. (2023): Monitoring and control of forest seedling quality in Europe. Forest Ecology and Management, 546: 121308. Go to original source...
    41. Miljković V., Gajski D. (2016): Adaptation of industrial hyperspectral line scanner for archaeological applications. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B5: 343-345. Go to original source...
    42. Miljković V., Gajski D., Vela E. (2017): Spatial calibration of the hyperspectral line scanner by the bundle block adjusting method. Geodetski list, 71: 127-142.
    43. Miljković V., Odak I., Babić L., Hećimović I. (2025): Hiperspektralna tehnologija i mogućnosti primjene. In: Racetin I., Zrinjski M., Župan R. (eds): Zbornik radova - 17. simpozij ovlaštenih inženjera geodezije, Poreč, Nov 23-26, 2024: 145-151. (in Croatian)
    44. Moghimi A., Yang C., Marchetto P.M. (2018): Ensemble feature selection for plant phenotyping: A journey from hyperspectral to multispectral imaging. IEEE Access, 6: 56870-56884. Go to original source...
    45. Neuwirthová E., Chuchlík J., Pikl M., Lhotáková Z., Kashkan I., Panzarová K., Stejskal J., Albrechtová J., Lstibůrek M., Čepl J. (2026): Overcoming difficulties in segmentation of hyperspectral plant images with small projection areas using machine learning. Scientific Reports, 16: 4325 Go to original source...
    46. Ngadi M.O., Liu L. (2010): Hyperspectral image processing techniques. In: Sun D.W. (ed.): Hyperspectral Imaging for Food Quality Analysis and Control. London, Academic Press: 93-118. Go to original source...
    47. Pandey P., Payn K.G., Lu Y., Heine A.J., Walker T.D., Acosta J.J., Young S. (2021): Hyperspectral imaging combined with machine learning for the detection of fusiform rust disease incidence in loblolly pine seedlings. Remote Sensing, 13: 3595. Go to original source...
    48. Paulus S., Mahlein A.K. (2020): Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale. Giga Science, 9: giaa090. Go to original source...
    49. Pieruschka R., Schurr U. (2022): Origins and drivers of crop phenotyping. In: Walter A. (ed.): Advances in plant phenotyping for more sustainable crop production. Cambridge, Burleigh Dodds Science Publishing: 1-25. Go to original source...
    50. Qian S.E. (2021): Hyperspectral satellites, evolution, and development history. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 7032-7056. Go to original source...
    51. Qian S.E. (2022): Overview of hyperspectral imaging remote sensing from satellites. In: Chang C.I. (ed.): Advances in Hyperspectral Image Processing Techniques. New York/Hoboken, Wiley IEEE Press: 41-66. Go to original source...
    52. Roscher R., Behmann J., Mahlein A.K., Dupuis J., Kuhlmann H., Plümer L. (2016): Detection of disease symptoms on hyperspectral 3D plant models. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3: 89-96. Go to original source...
    53. Ruett M., Junker Frohn L.V., Siegmann B., Ellenberger J., Jaenicke H., Whitney C., Luedeling E., Tiede Arlt P., Rascher U. (2022): Hyperspectral imaging for high-throughput vitality monitoring in ornamental plant production. Scientia Horticulturae, 291: 110546. Go to original source...
    54. Sarić R., Nguyen V.D., Burge T., Berkowitz O., Trtílek M., Whelan J., Lewsey M.G., Čustović E. (2022): Applications of hyperspectral imaging in plant phenotyping. Trends in Plant Science, 27: 301-315. Go to original source... Go to PubMed...
    55. Singh L., Mafongoya P., Mutanga O., Peerbhay K.Y., Dovey S. (2021): Detecting nutrient deficiencies in Eucalyptus grandis trees using hyperspectral remote sensing and random forest. South African Journal of Geomatics, 10: 207-222. Go to original source...
    56. Song D., De Silva K., Brooks M.D., Kamruzzaman M. (2023): Biomass prediction based on hyperspectral images of the Arabidopsis canopy. Computers and Electronics in Agriculture, 210: 107939. Go to original source...
    57. Stejskal J., Čepl J., Neuwirthová E., Akinyemi O.O., Chuchlík J., Provazník D., Keinänen M., Campbell P., Albrechtová J., Lstibůrek M., Lhotáková Z. (2023): Making the genotypic variation visible: Hyperspectral phenotyping in Scots pine seedlings. Plant Phenomics, 5: 0111. Go to original source... Go to PubMed...
    58. Stuart M.B., Davies M., Hobbs M.J., Pering T.D., McGonigle A.J.S., Willmott J.R. (2022): High-resolution hyperspectral imaging using low-cost components: Application within environmental monitoring scenarios. Sensors, 22: 4652. Go to original source... Go to PubMed...
    59. Tao H., Feng H., Xu L., Miao M., Yang G., Yang X., Fan L. (2020): Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images. Sensors, 20: 1231. Go to original source...
    60. Treitz P.M., Howarth P.J. (1999): Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems. Progress in Physical Geography, 23: 359-390. Go to original source...
    61. Wahabzada M., Mahlein A.K., Bauckhage C., Steiner U., Oerke E.C., Kersting K. (2016): Plant phenotyping using probabilistic topic models: Uncovering the hyperspectral language of plants. Scientific Reports, 6: 22482. Go to original source...
    62. Xing H., Feng H., Fu J., Xu X., Yang G. (2019): Development and application of hyperspectral remote sensing. In: Computer and Computing Technologies in Agriculture XI: Proceedings of the 11th IFIP WG 5.14 International Conference, CCTA 2017, Part II, Jilin, Aug 12-15, 2017: 271-282. Go to original source...
    63. Xing D., Sun P., Wang Y., Jiang M., Miao S., Liu W., Huang H., Lin E. (2024): Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra. Forestry Research, 4: e024. Go to original source... Go to PubMed...
    64. Yang Z., Tian J., Feng K., Gong X., Liu J. (2021): Application of a hyperspectral imaging system to quantify leaf-scale chlorophyll, nitrogen and chlorophyll fluorescence parameters in grapevine. Plant Physiology and Biochemistry, 166: 839-850. Go to original source...
    65. Yang D., Song J., Huang C., Yang F., Han Y., Wang R. (2025): A study on airborne hyperspectral tree species classification based on the synergistic integration of machine learning and deep learning. Forests, 16: 1032. Go to original source...
    66. Yue Z., Zhang Q., Zhu X., Zhou K. (2024): Chlorophyll content estimation of Ginkgo seedlings based on deep learning and hyperspectral imagery. Forests, 15: 2010. Go to original source...
    67. Zhao Y., Fan J., Lu X., Zhang Y., Wen W., Huang G., Li Y., Guo X., Chen L. (2025): From leaf to canopy: Inversion of lettuce pigment distribution using hyperspectral imaging technology combined with deep learning algorithms. Plant Physiology and Biochemistry, 198: 100104. Go to original source... Go to PubMed...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return