Strategy for Monitoring the Blast Incidence in Crops of Bomba Rice Variety Using Remote Sensing Data

Alba Agenjos-Moreno, Constanza Rubio (), Antonio Uris, Rubén Simeón, Belén Franch, Concha Domingo and Alberto San Bautista
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Alba Agenjos-Moreno: Department of Plant Production, Universitat Politècnica de València, 46022 Valencia, Spain
Constanza Rubio: Physics Technologies Research Centre, Universitat Politècnica de València, 46022 Valencia, Spain
Antonio Uris: Physics Technologies Research Centre, Universitat Politècnica de València, 46022 Valencia, Spain
Rubén Simeón: Department of Plant Production, Universitat Politècnica de València, 46022 Valencia, Spain
Belén Franch: Global Change Unit, Image Processing Laboratory, Universitat de València, 46980 Valencia, Spain
Concha Domingo: Rice Department, Genomic Centre, Instituto Valenciano de Investigaciones Agrarias (IVIA), Carretera CV-315. km 10.7, 46113 Moncada, Spain
Alberto San Bautista: Department of Plant Production, Universitat Politècnica de València, 46022 Valencia, Spain

Agriculture, 2024, vol. 14, issue 8, 1-17

Abstract: In this paper, we investigated the monitoring and characterization of the pest Magnaporthe oryzae , known as rice blast, in the Bomba rice variety at the Albufera Natural Park, located in Valencia, Spain during the 2022 and 2023 seasons. Using reflectance data from different Sentinel-2 satellite bands, various vegetative indices were calculated for each year. Significant differences in reflectance in the visible (B4), infrared (B8), red-edge (B6 and B7), and SWIR (B11) bands were detected between healthy and unhealthy fields. Additionally, variations were observed in the vegetation indices, with RVI and IRECI standing out for their higher accuracy in identifying blast-affected plots compared to NDVI and NDRE. Early differences in band values, vegetative indices, and spectral signatures were observed between the unhealthy and healthy plots, allowing for the anticipation of control treatments, whose effectiveness relies on timely intervention.

Keywords: rice; Magnaporthe oryzae; remote sensing; vegetative indices (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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