TRAMPA ELECTRÓNICA IOT PARA EL MONITOREO DE SPODOPTERA FRUGIPERDA EN CULTIVOS DE MAÍZ (IOT ELECTRONIC TRAP FOR MONITORING SPODOPTERA FRUGIPERDA IN CORN CROPS)
Resumen
El manejo integrado de plagas en la agricultura es fundamental para aumentar rendimientos en los cultivos, disminuir costos de producción y mejorar la sustentabilidad ambiental. Este artículo muestra los avances en el desarrollo de trampas electrónicas inteligentes para el monitoreo de la incidencia del gusano cogollero (Spodoptera Frugiperda) en un cultivo de maíz. El artículo se centra en el diseño de la arquitectura del sistema y el desarrollo de algoritmos de procesamiento de imágenes para la detección y el conteo de insectos. El seguimiento de la población de insectos se transmite a una estación base y se monitorea mediante dispositivos con acceso a internet. En los resultados preliminares de detección de polillas se encontró una precisión de 96.1% mediante una red neuronal convolucional y 100% con tratamiento digital de imágenes. Se evidencia la potencialidad del sistema desarrollado para el seguimiento de esta plaga en el cultivo de maíz.
Palabras Claves: Agricultura, insectos, maíz, redes de sensores inalámbricas, tratamiento de imágenes.
Abstract
Integrated pest management in agriculture is essential for increasing crop yields, reducing production costs, and improving environmental sustainability. This article presents advances in the development of intelligent electronic traps for monitoring the incidence of fall armyworm (Spodoptera frugiperda) in a corn crop. The article focuses on the design of the system architecture and the development of image processing algorithms for insect detection and counting. Insect population monitoring data are transmitted to a base station and monitored using devices with internet access. Preliminary results for moth detection showed 97% accuracy using a convolutional neural network and 100% accuracy with digital image processing. The potential of the developed system for monitoring this pest in corn cultivation is evident.
Keywords: Agriculture, corn, image processing, insects, wireless sensor networks.
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