Neuro-fuzzy operational performance of a coffee harvester machine

The objective of this work was to develop and to evaluate neuro-fuzzy systems as a methodology to describe coffee harvester machine operational performance when compared to multiple regression models. It was considered as input variables fruit maturation index, in the levels of 75.70, 87.00, 98.70%, operational speed, in the levels of 0.16, 0.26, 0.57m.s-1 and rods vibration, in the frequencies of 13.33, 15.00, 16.66, 18.33Hz. Coffee fruit harvest efficiency and plant leaf fall were considered as output variables. Hybrid neural network training was applied to input and output data in order to optimize fuzzy systems parameters for coffee fruit harvest efficiency and plant leaf fall prediction. Neuro-fuzzy models presented better performance when compared to multiple regression models. Based on developed neuro-fuzzy systems control maps, levels of speed and vibration could be recommended according to fruit maturation stage in the field.