Identificación del estado fitosanitario de árboles mediante aprendizaje automático e imágenes de muy alta resolución espacial
DOI:
https://doi.org/10.17268/sci.agropecu.2024.013Palabras clave:
agricultura de precisión, árboles enfermos, cítricos; naranja, México, vehículo aéreo no tripuladoResumen
Las enfermedades de los árboles contribuyen a importantes pérdidas económicas y de alimentos en el sector agrícola. La detección temprana de problemas fitosanitarios en árboles con métodos no destructivos resulta fundamental para garantizar la producción sostenible de naranja. Este trabajo presenta los resultados de una metodología diseñada para la identificación de árboles de naranja enfermos en una huerta ubicada en el cinturón citrícola de México, particularmente en la región de Rioverde, San Luis Potosí. Para ello, se tomaron imágenes con una cámara multiespectral de muy alta resolución espacial instalada en un vehículo aéreo no tripulado con las que se construyó un ortomosaico georreferenciado. Se emplearon seis clases temáticas para identificar los diferentes niveles de sanidad. Se utilizaron diferentes algoritmos de clasificación supervisada a nivel píxel que incluyen Random Forest (RF), K-Nearest Neighbor (KNN), Spectral Angle Mapper (SAM), Support Vector Machine (SVM), y Maximum Likelihood (ML). Considerando la exactitud de clasificación obtenida por cada uno de los algoritmos, se pueden ordenar de la siguiente manera: Maximum Likelihood (ML) con un 88,10%, Support Vector Machine (SVM) con un 77,38%, Spectral Angle Mapper (SAM) con un 76,19%, K-Nearest Neighbor (KNN) con un 64,68% y Random Forest (RF) con un 61,90%. Los resultados permitieron identificar el estado fitosanitario de todos los árboles de la huerta, con una exactitud aceptable y representan información valiosa de manejo para el productor.
Citas
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