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MS-YieldStackNet: fusión de datos multifuente para la estimación del rendimiento del trigo utilizando una red

Waqas Ali¹, Zeeshan Ramzan², Muhammad Shahbaz³

¹Department of Computer Science, University of Engineering and Technology Lahore, Lahore, Pakistan.

PeerJ. Computer science

|January 22, 2026

Resumen

Este resumen es generado por máquina.

Este estudio presenta MS-YieldStackNet, un marco novedoso para la predicción precisa del rendimiento del trigo utilizando datos satelitales y análisis del suelo. El modelo mejora la seguridad alimentaria y la planificación agrícola en regiones como Pakistán.

Palabras clave:

inteligencia artificial aprendizaje de conjunto seguridad alimentaria multimodal teledetección estimación de rendimiento

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Área de la Ciencia:

Ciencia Agrícola; Teledetección; Aprendizaje Automático

Sus antecedentes:

La predicción precisa del rendimiento de los cultivos es crucial para la seguridad alimentaria y la política agrícola.; Los métodos manuales de estimación del rendimiento del trigo son laboriosos e imprecisos, especialmente en Pakistán.; La integración de diversas fuentes de datos puede mejorar la precisión de la previsión del rendimiento.

Objetivo del estudio:

Desarrollar y validar un marco algorítmico novedoso, MS-YieldStackNet, para la predicción del rendimiento del trigo de alta resolución.; Integrar imágenes satelitales multiespectrales, análisis in situ del suelo y variables meteorológicas para mejorar la previsión.; Evaluar el rendimiento del modelo utilizando métricas estadísticas clave.

Principales métodos:

Se construyó un espacio de características unificado utilizando índices de vegetación (NDVI, DVI), atributos fisicoquímicos del suelo y datos climáticos temporales.; Se empleó una arquitectura de red neuronal de conjunto apilado (MS-YieldStackNet) que combina tres redes neuronales de retroalimentación (FFNN) paralelas.; Se utilizó un meta-aprendiz de Random Forest para integrar las predicciones de las FFNN.

Principales resultados:

Se logró un valor robusto de R-cuadrado de 0.81, lo que indica un fuerte rendimiento del modelo.; Se reportó un Error Cuadrático Medio (MSE) de 6114.30 kg/ha y un Error Cuadrático Medio (RMSE) de 78.19 kg/ha.; Se demostraron bajos errores de predicción con un Error Absoluto Medio (MAE) de 59.07 kg/ha y un Error Porcentual Absoluto Medio (MAPE) de 3.55%.

Conclusiones:

MS-YieldStackNet proporciona una solución precisa y escalable para la previsión del rendimiento del trigo.; El enfoque integrado mejora significativamente la precisión de la predicción en comparación con los métodos tradicionales.; El marco tiene un gran potencial para informar la política agrícola y garantizar la seguridad alimentaria.