gesjGEOGRAPHY, ENVIRONMENT, SUSTAINABILITYGEOGRAPHY, ENVIRONMENT, SUSTAINABILITY2071-93882542-1565Russian Geographical Society10.24057/2071-9388-2026-4712gesj-5059Research ArticleRESEARCH PAPERСтатьиForecasting wheat leaf disease development using spectral signature dynamics from satellite and ground-based dataSeredaIgor I.

Abovyan 68, Yerevan, 0025

igor.sereda@cens.amKremnevaOksana Yu.

62 Kalinina Street, Krasnodar, 350039

DanilovRoman Yu.

62 Kalinina Street, Krasnodar, 350039

GasiyanKsenia E.

62 Kalinina Street, Krasnodar, 350039

ZiminMikhail V.

1 Leninskie Gory, Moscow, 119991

29 Staromonetny Lane, Moscow, 119017

 

Center for Ecological-Noosphere Studies of the National Academy of Sciences of the Republic of ArmeniaArmeniaFederal State Budgetary Scientific Institution “Federal Research Center of Biological Plant Protection”Russian FederationFaculty of Geography, Lomonosov Moscow State University; Institute of Geography, Russian Academy of SciencesRussian Federation202609102026193188203Copyright © Sereda I.I., Kremneva O.Y., Danilov R.Y., Gasiyan K.E., Zimin M.V., 20262026Sereda I.I., Kremneva O.Y., Danilov R.Y., Gasiyan K.E., Zimin M.V.Sereda I.I., Kremneva O.Y., Danilov R.Y., Gasiyan K.E., Zimin M.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ges.rgo.ru/jour/article/view/5059

In the context of modern agricultural land management systems, precision agriculture is becoming increasingly relevant. Remote sensing is one of the most promising sources of operational crop information. Nevertheless, despite the growing interest, there is still a lack of research to make the transition from theoretical knowledge to the precise practical application of fungicides. This study evaluates whether integrating satellite remote sensing with ground-based spectrometry can reliably predict early-stage development of the pathogenic background in winter wheat, including overall disease pressure and specific pathogens, under real field conditions in both conventional tillage and no-till systems, with the goal of supporting precision farming applications. The experiment was conducted in the experimental fields of the research institute in Krasnodar Krai, Russia. Test plots were created with different methods of pre-sowing soil cultivation (conventional tillage and no-till farming), as well as with an artificial infectious background and disease-free control plots. The study used data from ground-based spectrometry and space imaging by the Planet Scope satellite constellation. The results indicate that spectral data are correlated with pathogen dynamics and allow predicting disease manifestation. Prediction accuracy is likely to improve with denser observations and more reliable input data. The most informative spectral ranges for detecting diseases were 443, 490 and 865 nm. This study describes a methodology for predicting winter wheat diseases and highlights the potential of using spectral data for precision agriculture.

winter wheatwheat pathogenphytosanitary monitoringremote sensinghyperspectral spectrometrycrop disease detectionThe authors gratefully acknowledge the support provided within the State Assignment of the Ministry of Education and Science of the Russian Federation, topic No. FGRN-2025-0007, under which ground-based spectrometry and visual assessment of wheat disease development were carried out. Special appreciation is given to the Center of Collective Usage “Geoportal, Lomonosov Moscow State University” for providing access to the ASD FieldSpec 3 Hi-Res spectroradiometer within the state budgetary theme No. 121051400061-9. We also extend our sincere thanks to the Institute of Geography, Russian Academy of Sciences, for additional support under State Assignment No. FMWS-20240009, No. 1023032700199-9, for providing satellite imagery data. These contributions were instrumental in the successful completion of this study.ReferencesAbaba, G. (2023). 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The authors declare that there are no conflicts of interest present.