Comparison of Prediction Models for Determining the Degree of Damage to Korla Fragrant Pears

Abstract

For a fast and accurate evaluation of the values of damaged fragrant pears, a prediction method of the damage degree of Korla fragrant pears was proposed. To study variation laws of damages of fragrant pears under different volumes of squeezing deformation, the partial least squares regression (PLSR), the generalised regression neural network (GRNN) and the adaptive neural fuzzy inference system (ANFIS) were chosen to predict the damage degree of fragrant pears and establish the optimal prediction model. The results demonstrated that with the increase of ripeness or deformation value, the damage degree of fragrant pears increases gradually. For performance comparison of prediction models based on PLSR, GRNN and ANFIS, it was found that the trained PLSR, GRNN and ANFIS can all predict the damage degree of Korla fragrant pears. The ANFIS, which inputs the membership function of dsigmf (R2 = 0.9979, RMSE = 46.6) and psigmf (R2 = 0.9979, RMSE = 46.6), achieves the best performance. Research results can provide theoretical references to the evaluation of the commodity value of damaged fragrant pears, quality grading of fragrant pears and design of the picking machine.