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Un modelo de clasificación de madurez para los jujubes de invierno basado en DSAF-ResNet

Yufei Song^1,2,3, Aoran Liu^4,5, Xi Meng³

¹College of Horticulture, Hebei Agricultural University, Baoding, China.

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Resumen

Este resumen es generado por máquina.

La clasificación precisa de la madurez de los jujubes de invierno se logra utilizando una nueva red residual de fusión de atención de doble corriente (DSAF-ResNet). Este método combina datos hiperespectrales y de textura para mejorar la recolección inteligente y el control de calidad en la agricultura.

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Área de la Ciencia:

Ingeniería agrícola
Visión por computadora
Aprendizaje automático

Sus antecedentes:

La clasificación precisa y no destructiva de la madurez de los jujubes de invierno es esencial para optimizar el momento de la cosecha y garantizar la calidad de los frutos.
Los métodos actuales a menudo carecen de la precisión necesaria para diferenciar las etapas sutiles de madurez, lo que afecta a los procesos posteriores a la cosecha.

Objetivo del estudio:

Desarrollar y evaluar un nuevo modelo de aprendizaje profundo para una clasificación precisa y no destructiva de la madurez de los jujubes de invierno.
Investigar la eficacia de la fusión de características hiperespectrales y de textura para mejorar la evaluación de la madurez.

Principales métodos:

Se propuso una red residual de atención fusionada de doble corriente (DSAF-ResNet), que integraba imágenes hiperespectrales y características de textura de la Matriz de Coocurrencia de Nivel Gris (GLCM).
La red incorporó los mecanismos de atención RepVGGBlock y SimAM dentro de una arquitectura de doble flujo.
El rendimiento del modelo se validó utilizando métricas de precisión, precisión y recuerdo de prueba.

Principales resultados:

El enfoque multimodal fusionado mejoró significativamente el rendimiento de la clasificación en comparación con los insumos de una sola modalidad.
El DSAF-ResNet logró una alta precisión de prueba (97,24%), precisión (97,31%) y recuerdo (97,24%).
Los estudios de ablación confirmaron la eficacia de los componentes individuales de la red y de la estrategia de fusión.

Conclusiones:

DSAF-ResNet ofrece un marco eficaz y escalable para la clasificación no destructiva de la madurez de las frutas.
Este enfoque mejora las prácticas agrícolas inteligentes y apoya la agricultura de precisión al permitir una evaluación sólida de la madurez.
El modelo demuestra una excelente generalización y estabilidad, incluso con conjuntos de datos desequilibrados.