Remote sensing and field analysis of Erwinia amylovora on quince
DOI:
https://doi.org/10.15835/nbha53314657Keywords:
climatic conditions, fire blight, frequency and intensity of attack, NDRE, NDVIAbstract
Fire blight is responsible for significant losses and affects production quality in all growing countries worldwide, including Romania. Despite the availability of various prevention and control strategies, their practical effectiveness has been notably limited, and no viable technologies have yet emerged. This research aims to investigate the impact of climatic conditions, genetic material and cultivation methods on the intensity of Erwinia amylovora attack on quince by using field determination techniques and satellite sensor systems. Over three years in a quince orchard naturally infected with E. amylovora, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge (NDRE) indices were tested along with analyses of attack intensity (I), degree of attack (DA) and frequency (F). The results obtained showed that the genetic sensitivity and/or tolerance of the varieties is essential, together with the specific local climatic conditions. The two analyzed varieties (‘Bereczky’ and ‘Aurii’) cultivated under organic technology, showed different levels of tolerance to fire blight. While a significant correlation exists between the two vegetation indices, NDRE demonstrates higher accuracy due to its ability to conduct spectral analyses within the plant canopy. Furthermore, NDRE values closely align with those observed through assessments of attack intensity and frequency in the orchard.
References
Abd El-Hameid SA, Eid NA, Abdalla, FM (2025). Effect of plant extracts and bacteria on productivity, quality, and control of fire blight of pear. Egyptian Journal of Desert Research 75(1):111-134. 10.21608/ejdr.2025.354568.1196
Aćimović SG, Santander RD, Meredith CL, Pavlović ŽM (2023). Fire blight rootstock infections causing apple tree death: A case study in high-density apple orchards with Erwinia amylovora strain characterization. Frontiers in Horticulture 2:1082204. https://doi.org/10.3389/fhort.2023.1082204
Al-Saddik H, Simon JC, Cointault F (2017). Development of spectral disease indices for ‘Flavescence Dorée’ grapevine disease identification. Sensors 17(12):2772. https://doi.org/10.3390/s17122772
Amankulova K, Farmonov N, Mukhtorov U, Mucsi L (2023). Sunflower crop yield prediction by advanced statistical modeling using satellite-derived vegetation indices and crop phenology. Geocarto International 38(1):2197509. https://doi.org/10.1080/10106049.2023.2197509
Ault TR, Schwartz MD, Zurita-Milla R, Weltzin JF, Betancourt JL (2015). Trends and natural variability of spring onset in the coterminous United States as evaluated by a new gridded dataset of spring indices. Journal of Climate 28:8363-8378. http://www.jstor.org/stable/26195697
Aziz D, Rafiq S, Saini P, Ahad I, Gonal B, Rehman SA, … Singh G (2025). Remote sensing and artificial intelligence: Revolutionizing pest management in agriculture. Frontiers in Sustainable Food Systems 9:1551460. https://doi.org/10.3389/fsufs.2025.1551460
Boiarskii B, Hasegawa H (2019). Comparison of NDVI and NDRE indices to detect differences in vegetation and chlorophyll content. Journal of Mechanics of Continua and Mathematical Sciences 4:20-29. https://doi.org/10.26782/jmcms.spl.4/2019.11.00003
Carneiro FM, Furlani CEA, Gírio LAS, Silva RP, Malheiros EB (2017). Mechanized and individualized application of N-P-K in combined operations in sugarcane. Engenharia Agrícola 37(4):676-688. http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v37n4p676-688/2017
Cui Z, Huntley R.B, Zeng Q, Steven B (2021). Inoculation of stigma colonizing microbes to apple stigmas alters microbiome structure and reduces the occurrence of fire blight disease. Phytobiomes Journal 5:156-165. https://doi.org/10.1094/PBIOMES-04-20-0035-R
D’Auria D, Ristorto G, Persia F, Vidoni R, Mazzetto F (2016). Reversible data hiding based on image interpolation with a secret message reduction strategy. International Journal of Computer & Software Engineering 1:103. https://doi.org/10.15344/2456-4451/2016/103
Dagher F, Olishevska S, Philion V, Zheng J, Déziel E (2020). Development of a novel biological control agent targeting the phyto pathogen Erwinia amylovora. Heliyon 6(10):e05222. https://doi.org/10.1016/j.heliyon.2020.e05222
Daglio G, Cesaro P, Todeschini, V, Lingua G, Lazzari M, Berta G, Massa N (2022). Potential field detection of Flavescence dorée and Esca diseases using a ground sensing optical system. Biosystems Engineering 215:203-214. https://doi.org/10.1016/j.biosystemseng.2022.01.009
Das PK, Laxman B, Rao SK, Seshasai MVR, Dadhwal VK (2015). Monitoring of bacterial leaf blight in rice using ground-based hyperspectral and LISS IV satellite data in Kurnool, Andhra Pradesh, India. International Journal of Pest Management 61:359-368. https://doi.org/10.1080/09670874.2015.1072652
Dougherty L, Wallis A, Cox K, Zhong G-Y, Gutierrez B (2021). Phenotypic evaluation of fire blight outbreak in the USDA Malus collection. Agronomy 11:144. https://doi.org/10.3390/agronomy11010144
EOS Data Analytics NDRE (2023). EOS NDRE. Retrieved 2025 March 10 from https://eos.com/industries/agriculture/ndre/
European Space Agency (2025). S2 MSI Product Specification. Retrieved 2025 March 12 from https://sentinel.esa.int/documents/247904/349490/S2
European Space Agency (2025). Sentinel-2 user handbook; ESA Standard Document; ESA: Paris, France, 2015; Retrieved 2025 March 12 from https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook
Fallah MA, Yadegari M, Nejad GD, Nemati SH (2025). The rootstock type affecting fruit yield, and quality, and fire blight resistance of ‘Dargazi’ and ‘Beyrouti’ pear genotypes. Discover Plants 2:137. https://doi.org/10.1007/s44372-025-00217-0
Martinelli E, Candeo A, Di Benedetto A, Yang N, Cremonesi M, Mederos-Henry F, Van der Snickt G, Nikiforv A, Comelli D (2024). Use of hyperspectral imaging to monitor the effectiveness of plasma-generated atomic oxygen for non-contact cleaning of indigo dyed silk. EPJ Web of Conferences 309:14008. https://doi.org/10.1051/epjconf/202430914008
Mazzia V, Comba L, Khaliq A, Chiaberge M, Gay P (2020). UAV and machine learning based refinement of a satellite-driven vegetation index for precision agriculture. Sensors 20(9):2530. https://doi.org/10.3390/s20092530
Meena SV, Dhaka VS, Sinwar D (2020). Exploring the role of vegetation indices in plant diseases identification. Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, India pp 372-377. https://doi.org/10.1109/PDGC50313.2020.9315814
Melnyk O, Brunn A (2025). Analysis of spectral index interrelationships for vegetation condition assessment on the example of wetlands in Volyn Polissya, Ukraine. Earth 6(2):28. https://doi.org/10.3390/earth6020028
Mendes RJ, Regalado L, Rezzonico F, Tavares F, Santos C (2024). Deciphering fire blight: From Erwinia amylovora ecology to genomics and sustainable control. Horticulturae 10(11):1178. https://doi.org/10.3390/horticulturae10111178
Naguib NS, Daliman S (2022). Analysis of NDVI and NDRE indices using satellite images for crop identification at Kelantan. In: 4th International Conference on Tropical Resources and Sustainable Sciences 2022 IOP Conf. Series: Earth and Environmental Science 1102:012054. https://doi.org/10.1088/1755-1315/1102/1/012054
Nicoletti R, De Masi L, Migliozzi A, Calandrelli MM (2024). Analysis of dieback in a coastal pinewood in Campania, Southern Italy, through high-resolution remote sensing. Plants 13:182. https://doi.org/10.3390/plants13020182
Oerke E (2020). Remote sensing of diseases. Annual Review of Phytopathology 58:225-252. https://doi.org/10.1146/annurev-phyto-010820-012832
Pádua L, Marques P, Adão T, Guimarães N, Sousa A, Peres E, Sousa JJ (2019). Vineyard variability analysis through UAV-based vigour maps to assess climate change impacts. Agronomy 9(10):581. https://doi.org/10.3390/agronomy9100581
Pârvu M (2010). Ghid practic de fitopatologie. Presa Universitară Clujeană, Cluj-Napoca, Romania.
Pedroncelli A, Puopolo G (2024). This tree is on fire: a review on the ecology of Erwinia amylovora, the causal agent of fire blight disease. Journal of Plant Pathology 106:823-837. https://doi.org/10.1007/s42161-023-01397-y
Pel C, Schenk M, Delbianco A, Vos S (2021). Pest survey card on Erwinia amylovora. EFSA Supporting Publications 18(7):6767E. https://doi.org/10.2903/sp.efsa.2021.EN-6767
Philion V, Trapman M (2011). Description and preliminary validation of RIMpro-Erwinia, a new model for fire blight forecast. Acta Horticulturae 896:307-318. https://doi.org/10.17660/ActaHortic.2011.896.43
Pimentel D (2021). Fire Blight Erwinia amylovora. Retrieved 2025 July 04 from https://dr-achbani.com/wp-content/uploads/2023/01/PaperB8-DIMAP_ErwiniaAmylovora_TechnologyTransferReport.pdf
Quiñones T, Stoof C, Newman-Thacker F, Jiménez A, Bezares F, Ramírez J, Cardil A (2025). Remotely sensed vegetation phenology drives large fire spread in northwestern Europe. International Journal of Wildland Fire 34(6):WF24079. https://doi.org/10.1071/WF24079
Reis-Pereira M, Tosin R, Martins RC, Dos Santos FN, Tavares F, Cunha M (2023). Enhancing kiwi bacterial canker leaf assessment: Integrating hyperspectral-based vegetation indexes in predictive modeling. Engineering Proceedings 48:22. https://doi.org/10.3390/CSAC2023-14920
Reis-Pereira M, Verrelst J, Tosin R, Rivera Caicedo JP, Tavares F, Neves dos Santos F, Cunha M (2024). Plant disease diagnosis based on hyperspectral sensing: Comparative analysis of parametric spectral vegetation indices and nonparametric Gaussian process classification approaches. Agronomy 14(3):493. https://doi.org/10.3390/agronomy14030493
Roșu-Mareș D, Chiorean A, Moldovan C, Guzu G, Cordea M, Florian V (2024). Comparative study of some Romanian and foreign apple cultivars response to natural infections with Erwinia amylovora (Burrill.). Journal of Central European Agriculture 25(3):760-766. https://doi.org/10.17930/AGL202226
Rougerie-Durocher S, Philion V, Szalatnay D (2020). Measuring and modelling of apple flower stigma temperature as a step towards improved fire blight prediction. Agricultural and Forest Meteorology 295:108171. https://doi.org/10.1016/j.agrformet.2020.108171
Sălceanu C, Paraschivu M, Cotuna O, Sărățeanu V, Olaru AL, Pǎunescu RA (2023). Economic importance and phytosanitary monitoring of fire blight. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development 23(3):796-801.
Santander RD, Biosca EG (2017). Erwinia amylovora psychrotrophic adaptations: evidence of pathogenic potential and survival at temperate and low environmental temperatures. PeerJ 5:e3931. https://doi.org/10.7717/peerj.3931
Schlathölter I, Broggini GAL, Streb S, Studer B, Patocchi A (2023). Field study of the fire blight resistant cisgenic apple line C44.4.146. Plant Journal113(6):1160-1175. https://doi.org/10.1111/tpj.16083
Sen S, Borkataki S, Maji S, Patel YK, Taye RR, Nanda SP (2025). Abiotic and biotic stress management in plants using different geoinformatics approaches. In: Smart Technologies in Sustainable Agriculture. Apple Academic Press pp 197-214.
Sestras AF, Pamfil D, Dan C, Bolboaca SD, Jäntschi L, Sestras RE (2011). Possibilities to improve apple scab (Venturia inaequalis (Cke.) Wint.) and powdery mildew [Podosphaera leucotricha (Ell. et Everh.) Salm.] resistance on apple by increasing genetic diversity using potentials of wild species. Australian Journal of Crop Science 5:748-755.
Sestras AF, Sestras RE, Barbos A, Militaru M (2008). The differences among pear genotypes to fire blight (Erwinia amylovora) attack, based on observations of natural infection. Notulae Botanici Horti Agrobotanici Cluj-Napoca 36:97-103. https://doi.org/10.15835/nbha36277
Shahi TB, Xu CYY, Neupane A, Guo W (2023). Recent advances in crop disease detection using UAV and deep learning techniques. Remote Sensing 15(9):2450. https://doi.org/10.3390/rs15092450
Simionca Mărcășan LI, Oltean I, Popa S, Plazas M, Vilanova S, Gramazio P, Sestras AF, Prohens J, Sestras RE (2023). Comparative analysis of phenotypic and molecular data on response to main pear diseases and pest attack in a germplasm collection. International Journal of Molecular Sciences 24:6239. https://doi.org/10.3390/ijms24076239
Singh P, Pandey PC, Petropoulos GP, Pavlides A, Srivastava PK, Koutsias N, Deng KAK, Bao Y (2020). Hyperspectral remote sensing in precision agriculture: Present status, challenges, and future trends. In: Pandey PC, Srivastava PK, Petropoulos GP (Eds). Hyperspectral Remote Sensing; Elsevier, Amsterdam, The Netherlands pp 121-146. https://doi.org/10.1016/b978-0-08-102894-0.00009-7
Skoneczny H, Kubiak K, Spiralski M, Kotlarz J, Mikiciński A, Puławska J (2020). Fire blight disease detection for apple trees: Hyperspectral analysis of healthy, infected and dry leaves. Remote Sensing 12:2101. https://doi.org/10.3390/rs1213210
Slack SM, Schachterle JK, Sweeney EM, Kharadi RR, Peng J, Botti-Marino M, Bardaji L, Pochubay EA, Sundin WGW (2022). In-orchard population dynamics of Erwinia amylovora on apple flower stigmas. Phytopathology 112:1214-1225. https://doi.org/10.1094/PHYTO-01-21-0018-R
Slack SM, Yuan X, Outwater CA, Sundin GW (2025). Identification of a reduced rate combination of a plant growth inhibitor with a plant defense inducer for the management of the shoot blight phase of fire blight. Plant Disease 109(5):1148-1157.
Solano-Alvarez N, Valencia-Hernández JA, Vergara-Pineda S, Millán-Almaraz JR, Torres-Pacheco I, Guevara-González RG (2022). Comparative analysis of the NDVI and NGBVI as indicators of the protective effect of beneficial bacteria in conditions of biotic stress. Plants 11:932. https://doi.org/10.3390/plants11070932
Sun W, Gong P, Zhao Y, Ming L, Zeng Q, Liu F (2023). Current situation of fire blight in China. Phytopathology 113(12):2143-2151. https://doi.org/10.1094/PHYTO-05-23-0170-RVW
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. https://doi.org/10.3390/s22030757
Treutter D (2005). Significance of flavonoids in plant resistance and enhancement of their biosynthesis. Plant Biology 7:581-591. https://doi.org/10.1055/s-2005-873009
Van Der Zwet T, Keil HL, and Keil HL (1979). Fire blight: A bacterial disease of rosaceous plants. No. 510. US Department of Agriculture,Washington D.C. p 200.
Vellingiri A, Kokila R, Nisha P, Kumar M, Chinnusamy S, Boopathi S (2025). Harnessing GPS, sensors, and drones to minimize environmental impact: Precision agriculture. In: Thandekkattu SG, Vajjhala VR (Eds). Designing sustainable internet of things solutions for smart industries. IGI Global pp 77-108. https://doi.org/10.4018/979-8-3693-5498-8.ch004
Vidican R, Mălinaș A, Ranta O, Moldovan C, Marian O, Ghețe A, Ghișe CR, Popovici F, Cătunescu GM (2023). Using remote sensing vegetation indices for the discrimination and monitoring of agricultural crops: A critical review. Agronomy 13:3040. https://doi.org/10.3390/agronomy13123040
Viljevac M, Dugalić K, Štolfa I, Đermić E, Cvjetković B, Sudar R, Kovačević J, Cesar V, Lepeduš H, Jurković Z (2009). Biochemical basis of apple leaf resistance to Erwinia amylovora infection. Food Technology and Biotechnology 47(3):281-287.
Voitik A, Kravchenko V, Pushka O, Kutkovetska T, Shchur T, Kocira S (2023). Comparison of NDVI, NDRE, MSAVI and NDSI indices for early diagnosis of crop problems. Agricultural Engineering 27:47-57. https://doi.org/10.2478/agriceng-2023-0004
Wallis AE, Cox KD (2020). Management of fire blight using pre-bloom application of prohexadione-calcium. Plant Disease 104(4):1048-1054. https://doi.org/10.1094/PDIS-09-19-1948-RE
Weiss M, Jacob F, Duveiller G (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment 236:111402. https://doi.org/10.1016/j.rse.2019.111402
Xu R, Adam L, Chapados J, Soliman A, Daayf F, Tambong JT Min (2021). ION Nanoporebased detection of Clavibacter nebraskensis, the corn Goss’s wilt pathogen, and bacteriomic profiling of necrotic lesions of naturally infected leaf samples. PLoS One 16:e0245333. https://doi.org/10.1371/journal.pone.0245333
Zepner L, Karrasch P, Wiemann F, Bernard L (2020). ClimateCharts.net an interactive climate analysis web platform. International Journal of Digital Earth 4(3):338-356. https://doi.org/10.1080/17538947.2020.1829112
Zevgolis YG, Kouris A, Christopoulos A, Dimitrakopoulos PG (2023). Linking thermal indices, productivity, phenotypic traits, and stressors for assessing the health of centennial traditional olive trees. Applied Sciences 13(20):11443. https://doi.org/10.3390/app132011443
Zhang J, Zhang Z, Wen Y, Zhu J, Wufuerjiang A, Tian J (2025). Evaluation of the efficacy of three antagonistic bacteria strains in the management of fire blight. International Journal of Molecular Sciences 26(9):4438. https://doi.org/10.3390/ijms26094438
Zhao H, Yang C, Guo W, Zhang L, Zhang D (2020) Automatic Estimation of crop disease severity levels based on vegetation index normalization. Remote Sensing 12:1930. https://doi.org/10.3390/rs12121930
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