Rice (Oryza sativa L.) is a strategic crop for global food security, supplying more than 20% of the calories consumed worldwide (Dahlgreen and Parr 2024). In Peru, rice cultivation is particularly ...

La mancha carmelita del arroz, asociada a Bipolaris oryzae, requiere esquemas de vigilancia que permitan detección y respuesta temprana. En este trabajo se presenta un esquema metodológico de vigilancia fitosanitaria en arroz basado en la integración de teledetección de proximidad y mediciones in situ en sectores agrícolas del departamento de Lambayeque (provincias de Lambayeque, Ferreñafe y Chiclayo). Se empleó un UAV DJI Matrice 300 RTK con cámara multiespectral (B, G, R, RedEdge y NIR; azul, verde, rojo, borde rojo e infrarrojo cercano) y sensor térmico H20T, complementado con captura de esporas, inspección visual del cultivo y registro meteorológico in situ. Las adquisiciones se organizaron en jornadas de monitoreo sucesivas entre marzo y junio de 2025. Los resultados preliminares muestran la generación de productos georreferenciados multiespectrales y térmicos y la obtención de evidencia fitosanitaria de referencia (severidad visual y cuantificación de esporas) para su integración con variables espectrales y térmicas. El avance evidencia la factibilidad del enfoque y sienta las bases para etapas posteriores de modelado con inteligencia artificial, integración de variables meteorológicas y generación de mapas de riesgo con proyección a un geoportal de apoyo a la decisión fitosanitaria.

Blueberry ( Vaccinium corymbosum L.) production represents one of the main pillars of Peruvian agro-exports; however, its sustainability is increasingly threatened by wood-infecting fungal diseases such as dieback. This study was conducted in the Nuevo Proyecto–Olmos area (Lambayeque, Peru), covering a total cultivated area of 176 ha. The objectives were to molecularly identify the fungal pathogens associated with blueberry dieback to perform a preliminary evaluation of the antagonistic potential of endophytic microorganisms. Pathogens were isolated and characterized using morphological and molecular approaches, followed by pathogenicity tests and in vitro efficacy assays of beneficial microorganisms. Disease incidence was dominated by Lasiodiplodia spp., followed by Neopestalotiopsis , Fusarium , and Diaporthe . Lasiodiplodia theobromae was the most prevalent and aggressive species, confirming its association with the observed disease symptoms. For the genus Lasiodiplodia , temperatures between 25 and 30 °C favored mycelial growth, whereas 20 °C and 35 °C limited development. Endophytic isolates of Trichoderma spp. and Bacillus spp. inhibited pathogen mycelial growth by more than 60% under in vitro conditions. These findings highlight the potential of beneficial microorganisms as preliminary candidates for the biological control of wood-infecting fungi in blueberry production systems.

1. The fall armyworm, Spodoptera frugiperda (J.E. Smith, 1797) (Lepidoptera: Noctuidae), is one of the most destructive pests of maize and other economically important crops in Peru and worldwide. ...

Rice brown spot, caused by Bipolaris oryzae, is an important constraint for rice production and requires timely field-scale monitoring. This study evaluated the use of multispectral bands acquired with a UAV-mounted sensor, together with vegetation indices, combined with machine-learning models to estimate rice brown spot severity under field conditions in Lambayeque and Cajamarca, Peru. A total of 37 sampling observations were collected across the vegetative, flowering, and milk-ripening stages. Spectral variables were extracted from UAV orthomosaics and related to field-based disease severity assessments. The strongest correlations with severity were observed for NDRE (r = −0.83) and NPCI (r = 0.77). Three regression models were evaluated using leave-one-out cross-validation (LOOCV): support vector regression with radial basis function kernel (SVR-rbf), support vector regression with linear kernel (SVR-linear), and Random Forest (RF). The SVR-linear model showed the lowest prediction error using NDRE, GREEN, and BLUE as predictors (R²_CV = 0.76; RMSE_CV = 1.31), although its performance was very similar to that of SVR-rbf and RF. These results indicate that UAV-derived multispectral information can support plot-level estimation of rice brown spot severity. However, model performance should be interpreted cautiously because of the small dataset, heterogeneous disease conditions, and moderate prediction accuracy. Further studies with larger and independent datasets are needed to improve robustness and transferability.