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Resumen
La Cuscuta spp. es una planta parásita que causa pérdidas estimadas en el 50 % del rendimiento de una amplia variedad de cultivos agrícolas, incluidos verduras, forrajes y árboles. En la búsqueda de alternativas para resolver este problema, los pequeños agricultores están explorando la aplicación de nuevas tecnologías en la producción de alimentos. Este trabajo presenta un modelo de clasificación de Cuscuta spp. con teledetección mediante imágenes aéreas recolectadas por UAV con las que se generan ortofotos donde se señalan las zonas infestadas. El modelo propuesto segmenta el color amarillento característico del tallo de Cuscuta spp. en el espacio de color HSV para llevar a cabo el proceso de entrenamiento de una Red Neuronal Convolucional (CNN) profunda. En los experimentos se incluyeron imágenes RGB de un cultivo de pimiento picante (Capsicum annuum Linnaeus) con presencia de Cuscuta spp. Además, realizamos la validación cruzada de 5 iteraciones del modelo con diferentes conjuntos de datos al emplear imágenes recolectadas durante tres semanas consecutivas para identificar el crecimiento de la zona afectada por la maleza. La arquitectura ResNet, de acuerdo con las métricas empleadas, resultó ser el mejor modelo para clasificar Cuscuta spp y no Cuscuta. El método propuesto permite a los pequeños productores identificar y localizar la maleza en las primeras fases de crecimiento para facilitar las labores de eliminación y mitigación.
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