Celem pracy był przegląd badań nad zastosowaniem spektroskopii odbiciowej – punktowej (spektroradiometria) i obrazowej (obra­zowanie hiperspektralne i multispektralne) – we wczesnym wykrywaniu chorób roślin dwuliściennych. Szczególną uwagę poświęcono trzem rodzinom roślin: psiankowatym (Solanaceae), kapustowatym (Brassicaceae) i bobowatym (Fabaceae). Na podstawie analizy ponad70 publikacji wykazano, że detekcja przedobjawowa została potwierdzona dla chorób grzybowych, grzybopodobnych, bakteryjnychi wirusowych na kilkunastu gatunkach roślin uprawnych, jednak polowa walidacja detekcji bezobjawowej ogranicza się do bardzo nielicz­nych układów patogen–roślina. W zdecydowanej większości badań rejestrowane zmiany spektralne odzwierciedlają reakcję fizjologiczną rośliny na porażenie, a nie bezpośrednią sygnaturę patogenu. Szerokie pasma spektralne w zakresie widzialnym (500–700 nm) i bliskiej podczerwieni (700–900 nm) najczęściej identyfikowano jako diagnostycznie istotne, przy czym przydatność poszczególnych zakresów zmienia się wraz z postępem choroby. Selekcja optymalnych długości fal i zaawansowane algorytmy klasyfikacyjne umożliwiają redukcję złożoności obliczeniowej i otwierają drogę do praktycznych zastosowań na platformach mobilnych.

This paper reviews the application of reflectance spectroscopy – both point-based (spectroradiometry) and imaging-based (hyperspec­tral and multispectral imaging) – for the early detection of diseases in dicotyledonous plants. Particular attention is devoted to three families of plants: Solanaceae, Brassicaceae, and Fabaceae. Based on the analysis of over 70 publications, pre-symptomatic detection has been confirmed for fungal, oomycete, bacterial, and viral diseases across several crop species; however, field validation of pre-symptom­atic detection remains limited to a very few pathogen–host systems. In the vast majority of studies, the recorded spectral changes reflect the physiological response of the plant to infection rather than a direct spectral signature of the pathogen. Broad spectral bands in the visible (500–700 nm) and near-infrared (700–900 nm) ranges were most frequently identified as diagnostically significant, with the utility of specific wavelength ranges changing with disease progression. Optimal wavelength selection and advanced classification algorithms enable computational complexity reduction and open the way to practical deployment on mobile platforms.

Adão T., Hypólito J., Almeida J., Gonçalves A.C., Peres E., Guimarães L., Sousa J.J. 2017. Hyperspectral imaging: a review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing 9 (11): 1110. DOI: 10.3390/ rs9111110

Anderegg J., Hund A., Karisto P., Mikaberidze A. 2019. In-field detection and quantification of Septoria tritici blotch in diverse wheat germplasm using spectral–temporal features. Frontiers in Plant Science 10: 1355. DOI: 10.3389/fpls.2019.01355

Arens N., Backhaus A., Döll S., Fischer S., Seiffert U., Mock H.-P. 2016. Non-invasive presymptomatic detection of Cercospora beticola infection and identification of early metabolic responses in sugar beet. Frontiers in Plant Science 7: 1377. DOI: 10.3389/fpls.2016.01377

Atsmon G., Nehurai O., Kizel F., Eizenberg H., Lati R.N. 2022. Hyperspectral imaging facilitates early detection of Orobanche cu­mana below-ground parasitism on sunflower under field conditions. Computers and Electronics in Agriculture 196 (2): 106881. DOI: 10.1016/j.compag.2022.106881

Bajwa S.G., Rupe J.C., Mason J. 2017. Soybean disease monitoring with leaf reflectance. Remote Sensing 9 (2): 127. DOI: 10.3390/rs9020127

Baranowski P., Jedryczka M., Mazurek W., Babula-Skowronska D., Siedliska A., Kaczmarek J. 2015. Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PLoS ONE 10 (3): e0122913. DOI: 10.1371/journal.pone.0122913

Barreto L.C., Martínez-Arias R., Schechert A. 2021. Field detection of Rhizoctonia root rot in sugar beet by near infrared spectrometry. Sensors 21 (23): 8068. DOI: 10.3390/s21238068

Brugger A., Yamati F.I., Barreto A., Paulus S., Schramowsk P., Kersting K., Steiner U., Neugart S., Mahlein A.-K. 2023. Hyper­spectral imaging in the UV range allows for differentiation of sugar beet diseases based on changes in secondary plant metabo­lites. Phytopathology 113 (1): 44–54. DOI: 10.1094/PHYTO-03-22-0086-R

Chen T., Yang W., Zhang H., Zhu B., Zeng R., Wang X., Wang S., Wang L., Qi H., Lan Y., Zhang L. 2020. Early detection of bac­terial wilt in peanut plants through leaf-level hyperspectral and unmanned aerial vehicle data. Computers and Electronics in Agriculture 177: 105708. DOI: 10.1016/j.compag.2020.105708

Cochavi A., Rapaport T., Gendler T., Karnieli A., Eizenberg H., Rachmilevitch S., Ephrath J.E. 2017. Recognition of Orobanche cumana below-ground parasitism through physiological and hyperspectral measurements in sunflower (Helianthus annuus L.). Frontiers in Plant Science 8: 909. DOI: 10.3389/fpls.2017.00909

Couture J.J., Singh A., Charkowski A.O., Groves R.L., Gray S.M., Bethke P.C., Townsend P.A. 2018. Integrating spectroscopy with potato disease management. Plant Disease 102 (11): 2233–2240. DOI: 10.1094/PDIS-01-18-0054-RE

de Queiroz Otone J.D., Theodoro G.F., Santana D.C., Teodoro L.P.R., de Oliveira J.T., de Oliveira I.C., da Silva Junior C.A., Teodoro P.E., Baio F.H.R. 2024. Hyperspectral response of the soybean crop as a function of target spot (Corynespora cassiicola) using machine learning to classify severity levels. AgriEngineering 6 (1): 330–343. DOI: 10.3390/agriengineer­ing6010020

Duan Z., Li H., Li C., Zhang J., Zhang D., Fan X., Chen X. 2024. A CNN model for early detection of pepper Phytophthora blight using multispectral imaging, integrating spectral and textural information. Plant Methods 20: 115. DOI: 10.1186/s13007-024- 01239-7

Fang Y., Ramasamy R.P. 2015. Current and prospective methods for plant disease detection. Biosensors 5 (3): 537–561. DOI: 10.3390/bios5030537

Feng L., Wu B., Chen S., Zhang C., He Y. 2022. Application of visible/near-infrared hyperspectral imaging with convolutional neural networks to phenotype aboveground parts to detect cabbage Plasmodiophora brassicae (clubroot). Infrared Physics & Technology 121 (6): 104040. DOI: 10.1016/j.infrared.2022.104040

Feng J., Zhang S., Zhai Z., Yu H., Xu H. 2024. DC²Net: an Asian soybean rust detection model based on hyperspectral imaging and deep learning. Plant Phenomics 6: 0163. DOI: 10.34133/plantphenomics.0163

Fones H.N., Bebber D.P., Chaloner T.M., Kay W.T., Steinberg G., Gurr S.J. 2020. Threats to global food security from emerging fungal and oomycete crop pathogens. Nature Food 1 (6): 332–342. DOI: 10.1038/s43016-020-0075-0

Franceschini M.H.D., Bartholomeus H., van Apeldoorn D., Suomalainen J., Kooistra L. 2017. Assessing changes in potato canopy caused by late blight in organic production systems through UAV-based pushbroom imaging spectrometer. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6: 109–112. DOI: 10.5194/isprs­-archives-XLII-2-W6-109-2017

Furlanetto R.H., Nanni M.R., Mizuno M.S., Crusiol L.G.T., Silva C.R.D. 2021. Identification and classification of Asian soy­bean rust using leaf-based hyperspectral reflectance. International Journal of Remote Sensing 42 (11): 4177–4198. DOI: 10.1080/01431161.2021.1890855

Gao L., Smith R.T. 2015. Optical hyperspectral imaging in microscopy and spectroscopy – a review of data acquisition. Journal of Biophotonics 8 (6): 441–456. DOI: 10.1002/jbio.201400051

Gold K.M., Townsend P.A., Chlus A., Herrmann I., Couture J.J., Larson E.R., Gevens A.J. 2020. Hyperspectral measurements en­able pre-symptomatic detection and differentiation of contrasting physiological effects of late blight and early blight in potato. Remote Sensing 12 (2): 286. DOI: 10.3390/rs12020286

Hernández-Castellano C.A., Rodrigo-Ilarri J., Rodrigo-Clavero M.-E., Martínez-Solano P.D. 2024. Hyperspectral image analysis and machine learning techniques for crop disease detection and identification: a review. Sustainability 16 (14): 6064. DOI: 10.3390/su16146064

Jiao Z., Zhang D., Zhang J., Wang L., Ma D., Ma L., Wang Y., Gu A., Fan X., Peng B., Shen S., Xuan S. 2025. Early detection of Chinese cabbage clubroot based on integrated leaf multispectral imaging and machine learning. Horticulturae 11 (11): 1335. DOI: 10.3390/horticulturae11111335

Jing B., Wang J., Zhang X., Hou X., Huang K., Wang Q., Wang Y., Jia Y., Feng M., Yang W., Wang C. 2025. Evaluating the poten­tial of airborne hyperspectral imagery in monitoring common beans with common bacterial blight at different infection stages. Biosystems Engineering 251: 145–158. DOI: 10.1016/j.biosystemseng.2025.02.002

Karadağ K., Tenekeci M.E., Taşaltın R., Bilgili A. 2020. Detection of pepper fusarium disease using machine learning algo­rithms based on spectral reflectance. Sustainable Computing Informatics and Systems 28: 100299. DOI: 10.1016/j.suscom. 019.01.001

Kong W., Zhang C., Cao F., Liu F., Luo S., Tang Y., He Y. 2018a. Detection of Sclerotinia stem rot on oilseed rape (Brassica napus L.) leaves using hyperspectral imaging. Sensors 18 (6): 1764. DOI: 10.3390/s18061764

Kong W., Zhang C., Huang W., Liu F., He Y. 2018b. Application of hyperspectral imaging to detect Sclerotinia sclerotiorum on oilseed rape stems. Sensors 18 (1): 123. DOI: 10.3390/s18010123

Krezhova D., Dikova B., Maneva S. 2014. Ground based hyperspectral remote sensing for disease detection of tobacco plants. Bulgarian Journal of Agricultural Science 20 (5): 1142–1150.

Krezhova D., Petrov N., Maneva S. 2012. Hyperspectral remote sensing applications for monitoring and stress detection in cultural plants: viral infections in tobacco plants. Proceedings of SPIE – The International Society for Optical Engineering 8531: 85311H-1. DOI: 10.1117/12.974722

Krezhova D., Velichkova K., Petrov N., Maneva S. 2017. The effect of plant diseases on hyperspectral leaf reflectance and bio­physical parameters. W: Proceedings of the Fifth International Conference on Radiation and Applications in Various Fields of Research 2: 269–275. (RAD 2017). Budva, Montenegro. DOI: 10.21175/RadProc.2017.55

Kuswidiyanto L.W., Kim D.E., Fu T., Kim K.S., Han X. 2023a. Detection of black spot disease on kimchi cabbage using hyper­spectral imaging and machine learning techniques. Agriculture 13 (12): 2215. DOI: 10.3390/agriculture13122215

Kuswidiyanto L.W., Wang P., Noh H.-H., Jung H.-Y., Jung D.-H., Han X. 2023b. Airborne hyperspectral imaging for early diag­nosis of kimchi cabbage downy mildew using 3D-ResNet and leaf segmentation. Computers and Electronics in Agriculture 214: 108312. DOI: 10.1016/j.compag.2023.108312

Leucker M., Wahabzada M., Kersting K., Peter M., Beyer W., Steiner U., Mahlein A.-K., Oerke E.-C. 2016. Hyperspectral imaging reveals the effect of sugar beet quantitative trait loci on Cercospora leaf spot resistance. Functional Plant Biology 44 (1): 1–9. DOI: 10.1071/FP16121

López M.M., Bertolini E., Olmos A., Caruso P., Gorris M.T., Llop P., Penyalver R., Cambra M. 2003. Innovative tools for detection of plant pathogenic viruses and bacteria. International Microbiology 6 (4): 233–243. DOI: 10.1007/s10123-003-0143-y

Lu J., Ehsani R., Shi Y., de Castro A.I., Wang S. 2018. Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor. Scientific Reports 8 (1): 2793. DOI: 10.1038/s41598-018-21191-6

Lu G., Fei B. 2014. Medical hyperspectral imaging: a review. Journal of Biomedical Optics 19 (1): 010901. DOI: 10.1117/1. JBO.19.1.010901

Lucieer A., Malenovský Z., Veness T., Wallace L. 2014. HyperUAS – imaging spectroscopy from a multirotor unmanned aircraft system. Journal of Field Robotics 31 (4): 571–590. DOI: 10.1002/rob.21508

Mahlein A.-K. 2016. Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease 100 (2): 241–251. DOI: 10.1094/PDIS-03-15-0340-FE

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 (1): 535–558. DOI: 10.1146/annurev-phyto-080417-050100

Mahlein A.-K., Steiner U., Hillnhütter C., Dehne H.-W., Oerke E.-C. 2012. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods 8: 3. DOI: 10.1186/1746-4811-8-3

Malthus T.J., Madeira A.C. 1993. High resolution spectroradiometry: Spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sensing of Environment 45 (1): 107–116. DOI: 10.1016/0034-4257(93)90086-D

Martínez-Martínez V., Gómez-Gil J., Machado M.L., Pinto F.A.C. 2018. Leaf and canopy reflectance spectrometry applied to the estimation of angular leaf spot disease severity of common bean crops. PLoS ONE 13 (4): e0196072. DOI: 10.1371/journal. pone.0196072

Marzougui A., Ma Y., Zhang C., McGee R.J., Coyne C.J., Main D., Sankaran S. 2019. Advanced imaging for quantitative evalua­tion of Aphanomyces root rot resistance in lentil. Frontiers in Plant Science 10: 383. DOI: 10.3389/fpls.2019.00383

Mishra P., Polder G., Vilfan N. 2020. Close range spectral imaging for disease detection in plants using autonomous platforms: a review on recent studies. Current Robotics Reports 1: 43–48. DOI: 10.1007/s43154-020-00004-7

Nagasubramanian K., Jones S., Sarkar S., Singh A.K., Singh A., Ganapathysubramanian B. 2018. Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems. Plant Methods 14: 86. DOI: 10.1186/s13007-018-0349-9

Nagasubramanian K., Jones S., Singh A.K., Sarkar S., Singh A., Ganapathysubramanian B. 2019. Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods 15: 98. DOI: 10.1186/s13007-019-0479-8

Oerke E.-C., Leucker M., Steiner U. 2019. Sensory assessment of Cercospora beticola sporulation for phenotyping the partial disease resistance of sugar beet genotypes. Plant Methods 15: 133. DOI: 10.1186/s13007-019-0521-x

Omran E.S.E. 2017. Early sensing of peanut leaf spot using spectroscopy and thermal imaging. Archives of Agronomy and Soil Science 63 (7): 883–896. DOI: 10.1080/03650340.2016.1247952

Qiao X., Wang J., Jing B., Zhang X., Jia Y., Huang K., Yang W., Feng M., Zhang Z., Zhao Y., Shafiq F., Xiao L., Song X., Zhang M., Wang C. 2025. Hyperspectral assessment of bacterial blight disease in red kidney beans by feature selection and machine learning algorithms. Precision Agriculture 26 (4): 60. DOI: 10.1007/s11119-025-10253-1

Ray S.S., Jain N., Arora R.K., Chavan S., Panigrahy S. 2011. Utility of hyperspectral data for potato late blight disease detection. Journal of the Indian Society of Remote Sensing 39 (2): 161–169. DOI: 10.1007/s12524-011-0094-2

Ribeiro W.R., Silva J.F.A., de Souza Almeida A., Lopes Ferreira A., Moreira Silva R., Alves T.M., Geraldine A.M., Valle Pinheiro P., Lobo Junior M. 2026. Early hyperspectral detection of Carlavirus vignae in common bean under field conditions. Tropical Plant Pathology. DOI: 10.21203/rs.3.rs-9694653/v1

Rumpf T., Mahlein A.-K., Steiner U., Oerke E.-C., Dehne H.-W., Plümer L. 2010. Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture 74 (1): 91–99. DOI: 10.1016/j.compag.2010.06.009

Sali M., Pippi L., Risoli S., Rossini M., Garzonio R., Savinelli B., Cotrozzi L., Cogliati S. 2025. UAV high-resolution hyperspectral imaging for monitoring Phomopsis stem canker in sunflowers. s. 156–161. W: 2025 IEEE International Workshop on Metrol­ogy for Agriculture and Forestry (MetroAgriFor), 28–30 October 2025. Institute of Electrical and Electronics Engineers Inc. DOI: 10.1109/MetroAgriFor66923.2025.11512333

Shao Y., Ji S., Xuan G., Ren Y., Feng W., Jia H., Wang Q., He S. 2024. Detection and analysis of chili pepper root rot by hyperspec­tral imaging technology. Agronomy 14 (1): 226. DOI: 10.3390/agronomy14010226

Strange R.N., Scott P.R. 2005. Plant disease: a threat to global food security. Annual Review of Phytopathology 43 (1): 83–116. DOI: 10.1146/annurev.phyto.43.113004.133839

Szechyńska-Hebda M., Hołownicki R., Doruchowski G., Sas K., Puławska J., Jarecka-Boncela A., Ptaszek M., Włodarek A. 2025. Application of hyperspectral imaging for early detection of pathogen-induced stress in cabbage as case study. Agronomy 15 (7): 1516. DOI: 10.3390/agronomy15071516

Terentev A., Dolzhenko V., Fedotov A., Eremenko D. 2022. Current state of hyperspectral remote sensing for early plant disease detection: a review. Sensors 22 (3): 757. DOI: 10.3390/s22030757

Thomas S., Kuska M.T., Bohnenkamp D., Brugger A., Alisaac E., Wahabzada M., Behmann J., Mahlein A.-K. 2018. Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. Journal of Plant Diseases and Protection 125 (1): 5–20. DOI: 10.1007/s41348-017-0124-6

Valencia-Ortiz M., McGee R.J., Sankaran S. 2025. Early detection of Aphanomyces root rot in pea plants using hyperspectral imag­ing. Physiological and Molecular Plant Pathology 140: 102862. DOI: 10.1016/j.pmpp.2025.102862

Van De Vijver R., Mertens K., Heungens K., Somers B., Nuyttens D., Borra-Serrano I., Lootens P., Roldán-Ruiz I., Vangeyte J., Saeys W. 2020. In-field detection of Alternaria solani in potato crops using hyperspectral imaging. Computers and Electronics in Agriculture 168: 105106. DOI: 10.1016/j.compag.2019.105106

Vane G., Goetz A.F.H. 1993. Terrestrial imaging spectrometry: Current status, future trends. Remote Sensing of Environment 44 (2–3): 117–126. DOI: 10.1016/0034-4257(93)90011-L

Veys C., Chatziavgerinos F., AlSuwaidi A., Hibbert J., Hansen M., Bernotas G., Smith M., Yin H., Rolfe S., Grieve B. 2019. Mul­tispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape. Plant Methods 15: 4. DOI: 10.1186/s13007- 019-0389-9

Wang A., Gao B., Cao H., Wang P., Zhang T., Wei X. 2022. Early detection of Sclerotinia sclerotiorum on oilseed rape leaves based on optical properties. Biosystems Engineering 224 (9): 80–91. DOI: 10.1016/j.biosystemseng.2022.09.005

Wang X., Zhang M., Zhu J., Geng S. 2008. Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN). International Journal of Remote Sensing 29 (6): 1693–1706. DOI: 10.1080/01431160701281007

Wójtowicz A. 2022. Zastosowanie teledetekcji hiperspektralnej do monitorowania porażenia roślin uprawnych przez patogeny. [Application of hyperspectral remote sensing for monitoring of crop infection by pathogens]. Progress in Plant Protection 62 (1): 66–75. DOI: 10.14199/ppp-2022-009

Xie C., Shao Y., Li X., He Y. 2015. Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging. Scientific Reports 5: 16564. DOI: 10.1038/srep16564

Xu J.-L., Gobrecht A., Héran D., Gorretta N., Coque M., Gowen A.A., Bendoula R., Sun D.-W. 2019. A polarized hyperspectral imaging system for in vivo detection: Multiple applications in sunflower leaf analysis. Computers and Electronics in Agricul­ture 158: 258–270. DOI: 10.1016/j.compag.2019.02.008

Yao Z., Lei Y., He D. 2019. Early visual detection of wheat stripe rust using visible/near-infrared hyperspectral imaging. Sensors 19 (4): 952. DOI: 10.3390/s19040952

Yu K., Anderegg J., Mikaberidze A., Karisto P., Mascher F., McDonald B.A., Walter A., Hund A. 2018. Hyperspectral canopy sens­ing of wheat Septoria tritici blotch disease. Frontiers in Plant Science 9: 1195. DOI: 10.3389/fpls.2018.01195

Zhang C., Chen T., Chen W., Sankaran S. 2022. Non-invasive evaluation of Ascochyta blight disease severity in chickpea using field-asymmetric ion mobility spectrometry and hyperspectral imaging techniques. Crop Protection 165: 106163. DOI: 10.1016/j.cropro.2022.106163

Zhang C., Chen W., Sankaran S. 2019. High-throughput field phenotyping of Ascochyta blight disease severity in chickpea. Crop Protection 125: 104885. DOI: 10.1016/j.cropro.2019.104885

Zhang X., Vinatzer B.A., Li S. 2024. Hyperspectral imaging analysis for early detection of tomato bacterial leaf spot disease. Scientific Reports 14: 27666. DOI: 10.1038/s41598-024-78650-6

Zhang N., Yang G., Pan Y., Yang X., Chen L., Zhao C. 2020. A review of advanced technologies and development for hyperspec­tral-based plant disease detection in the past three decades. Remote Sensing 12 (19): 3188. DOI: 10.3390/rs12193188

Zhao Y.-R., Yu K.-Q., Li X., He Y. 2016. Detection of fungus infection on petals of rapeseed (Brassica napus L.) using NIR hyper­spectral imaging. Scientific Reports 6: 38878. DOI: 10.1038/srep38878

Zhou C.A., Zheng L., Meng K., Zheng W., Zhang K., Shi Q. 2025. Early detection of tomato leaf spot and wilt diseases based on hyperspectral imaging technology. Vegetable Research 5: e026. DOI: 10.48130/vegres-0025-0010

Zhu H., Chu B., Zhang C., Liu F., Jiang L., He Y. 2017. Hyperspectral imaging for presymptomatic detection of tobacco disease with successive projections algorithm and machine-learning classifiers. Scientific Reports 7: 4125. DOI: 10.1038/s41598-017- 04501-2