Using Artificial Neural Network Application in Modelling the Mechanical Properties of Loading Position and Storage Duration of Pear Fruit

Abstract

In the study, rupture energy values of Deveci and Abate Fetel pear fruits were predicted using Artificial Neural Network (ANN). The breaking energy of the pears was examined in terms of storage time and loading position, and the experiments were carried out in two stages with samples kept in cold storage immediately after harvest and 30 days later. Rupture energy values (output data) were estimated using four different single and multilayer ANN models. -Four different model results obtained using Levenberg - Marquardt, Scaled Conjugate Gradient and resilient backpropagation training algorithms were compared with the calculated values. Statistical parameters such as R2, RMSE, MAE and MSE were used to evaluate the performance of the methods. Model 1 by ANN gave better results in network 5-1 the R2 value is 0.90, the square of the root error is 0.018, and 0.093 in the MAE is obtained using three inputs. © 2022 TAE 2022 - Proceeding of the 8th International Conference on Trends in Agricultural Engineering 2022. All rights reserved.