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MoistureVision: Predicción rápida no destructiva de la humedad de semillas de algodón mediante imágenes

Hao Li¹, Qiao Liu², Zhiguo Han³

¹College of Mechanical and Electrical Engineering, Shihezi University, ShiHezi 832003, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|January 30, 2026

Resumen

La predicción precisa de la humedad de las semillas de algodón utiliza aprendizaje automático e imágenes hiperespectrales. El modelo de regresión lineal múltiple LASSO de Savitzky-Golay ofrece una solución rápida y no destructiva para la evaluación de la calidad de las semillas.

Sus antecedentes:

El contenido preciso de humedad de las semillas de algodón es crucial para la vitalidad y el almacenamiento de las semillas.
Los métodos actuales de determinación de la humedad pueden ser lentos o destructivos.
El desarrollo de técnicas rápidas y no destructivas es esencial para la agricultura moderna.

Conclusiones:

El modelo SG-LASSO-MLR proporciona un método factible y eficaz para la detección rápida y no destructiva del contenido de humedad de las semillas de algodón.
Las imágenes hiperespectrales combinadas con ML ofrecen una herramienta potente para la visualización de la distribución espacial de la humedad dentro de las semillas.
Estos hallazgos respaldan los avances en los sistemas de monitoreo inteligente de cultivos y evaluación de la calidad de las semillas.

Palabras clave:

Semilla de algodón Imágenes hiperespectrales Aprendizaje automático Contenido de humedad Detección no destructiva

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