Estudio de aplicabilidad de técnicas de inteligencia artificial en el sector agropecuario

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01[Resumo] O aprendizaje máquina é unha rama da intelixencia artificial (IA) que utiliza algoritmos para realizar tarefas, sen que se teña programado explícitamente. Para o seu funcionamento require un proceso de formación e validación baseado en exemplos. Nesta tese proponse estudar a aplicabilidade dalgunhas técnicas de IA na produción agrícola. A tese é apoiada por tres publicacións cun importante factor de impacto JCR. Dous deles fan referencia a unha base de datos de produción de aves de ovos e outra a unha base de datos sobre a industrialización da cana de azucre. Na produción avícola estas técnicas foron estudadas para a alerta precoz dos problemas na curva de produción. En canto á aplicación destas técnicas no proceso industrial de cana de azucre, optimizáronse os modelos de calibración dos espectros NIR para o control de calidade nunha fábrica de azucre. Usáronse máquinas de soporte vectorial e redes neuronais artificiais. A aplicación destas técnicas ten un alto potencial de uso na produción agrícola, xa que posibilita o desenvolvemento de sistemas intelixentes de apoio ás decisións produtivas.[Resumen] El aprendizaje máquina es una rama de la inteligencia artificial (IA) que utiliza algoritmos para realizar tareas, sin que hayan sido programados de manera explícita. Para su funcionamiento se requiere de un proceso de entrenamiento y validación en base a ejemplos. En esta Tesis Doctoral, se propone estudiar la aplicabilidad de algunas técnicas de IA en la producción agropecuaria. El trabajo está respaldado por tres publicaciones con un importante factor de impacto JCR. Dos de ellas se refieren a una base de datos de producción avícola de huevos y la otra, a una base de datos de la industrialización de la caña de azúcar. En la producción avícola estas técnicas fueron estudiadas para la alerta temprana de problemas en la curva de producción. En cuanto a la aplicación de estas técnicas en el proceso industrial de la caña de azúcar, se optimizó los modelos de calibración de los espectros NIR para el control de calidad en una fábrica de azúcar. Se utilizó Máquinas de Soporte Vectorial y Redes de Neuronas Artificiales. La aplicación de estas técnicas tiene un alto potencial de uso en la producción agropecuaria, ya que posibilita el desarrollo de sistemas inteligentes de apoyo a las decisiones productivas[Abstract] Machine learning is a branch of artificial intelligence that uses algorithms to perform tasks, without having been programmed explicitly. For its operation requires a process of training and validation based on examples. In this thesis the application of artificial intelligence techniques in agricultural production is studied. As main result of the thesis, three articles has been published in journals with important JCR impact factors. Two of them refer to a database of poultry production of eggs and the other to a database of the industrialization of sugar cane. In poultry production these techniques were studied for the early warning of problems in the production curve. For the application of these techniques in the industrial process of sugarcane, the calibration models of the NIR spectra for the quality control in a sugar factory were optimized. In this work were used Support Vector Machines and Artificial Neural Networks. The application of these techniques has a high potential of use in the agricultural production, since it opens up the development of intelligent systems to support productive decisions

A Online NIR Sensor for the Pilot-Scale Extraction Process in Fructus Aurantii Coupled with Single and Ensemble Methods

Model performance of the partial least squares method (PLS) alone and bagging-PLS was investigated in online near-infrared (NIR) sensor monitoring of pilot-scale extraction process in Fructus aurantii. High-performance liquid chromatography (HPLC) was used as a reference method to identify the active pharmaceutical ingredients: naringin, hesperidin and neohesperidin. Several preprocessing methods and synergy interval partial least squares (SiPLS) and moving window partial least squares (MWPLS) variable selection methods were compared. Single quantification models (PLS) and ensemble methods combined with partial least squares (bagging-PLS) were developed for quantitative analysis of naringin, hesperidin and neohesperidin. SiPLS was compared to SiPLS combined with bagging-PLS. Final results showed the root mean square error of prediction (RMSEP) of bagging-PLS to be lower than that of PLS regression alone. For this reason, an ensemble method of online NIR sensor is here proposed as a means of monitoring the pilot-scale extraction process in Fructus aurantii, which may also constitute a suitable strategy for online NIR monitoring of CHM

A Online NIR Sensor for the Pilot-Scale Extraction Process in Fructus Aurantii Coupled with Single and Ensemble Methods

Model performance of the partial least squares method (PLS) alone and bagging-PLS was investigated in online near-infrared (NIR) sensor monitoring of pilot-scale extraction process in Fructus aurantii. High-performance liquid chromatography (HPLC) was used as a reference method to identify the active pharmaceutical ingredients: naringin, hesperidin and neohesperidin. Several preprocessing methods and synergy interval partial least squares (SiPLS) and moving window partial least squares (MWPLS) variable selection methods were compared. Single quantification models (PLS) and ensemble methods combined with partial least squares (bagging-PLS) were developed for quantitative analysis of naringin, hesperidin and neohesperidin. SiPLS was compared to SiPLS combined with bagging-PLS. Final results showed the root mean square error of prediction (RMSEP) of bagging-PLS to be lower than that of PLS regression alone. For this reason, an ensemble method of online NIR sensor is here proposed as a means of monitoring the pilot-scale extraction process in Fructus aurantii, which may also constitute a suitable strategy for online NIR monitoring of CHM