Detección de plantas de azafrán infestadas por ácaros mediante imágenes aéreas y un clasificador de aprendizaje automático
Resumen
Objetivo del estudio: Evaluar y desarrollar un código de aprendizaje automático que utilice imágenes aéreas en los espectros visible e infrarrojo cercano (NIR) para detectar plantas de azafrán (Crocus sativus L.) infestadas por ácaros mediante el procesamiento de índices espectrales para clasificar plantas sanas y enfermas. Esto permite identificar los puntos de concentración de los ácaros del bulbo y estimar el porcentaje de infestación en el campo.
Área de estudio: Provincia de Jorasán-Razaví, Torbat-Heydarieh, Irán.
Materiales y métodos: Cinco campos fueron seleccionados al azar, y se tomaron sus imágenes en rojo-verde-azul (RGB), como una imagen espectral visible típica, e imágenes en infrarrojo cercano (NIR) en dos años consecutivos. Se extrajeron y analizaron siete índices de vegetación espectrales para las imágenes NIR, que incluyeron NIR-band, redband, normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), difference Red-NIR ratio (DRN) and infrared percentage vegetation index (IPVI); y doce índices para las imágenes visibles RGB, que incluyeron red-band, green-band, blue-band, visible-band difference vegetation index (VDVI), visible atmospheric resistant index (VARI), triangular greenness index (TGI), normalized difference greenness index (NDGI), normalized green blue difference index (NGBDI), modified green red vegetation index (MGRVI), red green blue vegetation index (RGBVI), vegetative index (VEG) and excess of green index (EXG). Para detectar las plantas afectadas, se utilizaron dos clasificadores de Máquinas de Soporte Vectorial (SVM) con núcleos de Función de Base Radial (RBF) de forma separada para las imágenes NIR y RGB.
Resultados principales: La precisión promedio de los modelos clasificadores SVM se estimó en un 82.3% para las imágenes NIR y un 91.4% para las imágenes visibles durante la fase de prueba. Además, la precisión de los modelos desarrollados al ser evaluados en campo con respecto al método de matriz de confusión fue del 75.6% y 80.3% para los modelos de clasificación de imágenes NIR y RGB, respectivamente.
Aspectos destacados de la investigación: Las imágenes RGB lograron distinguir plantas infestadas con mejor precisión. El procesamiento de imágenes aéreas de drones de bajo peso podría acelerar la inspección de grandes campos de azafrán.
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