PREDICTIVE MODEL FOR WILD BLUEBERRY FRUIT LOSSES DURING HARVESTING

Abstract

Abstract. Wild blueberries are one of the most important fruit crops of Canada, producing more than 50% of the world’s production. Understanding and predicting the relationships between the machine operating parameters, fruit losses, topographic features and crop characteristics can aid in better berry recovery during mechanical harvesting. This paper suggested a modeling approach for prediction of fruit losses during harvesting using artificial neural network (ANN) and multiple regression (MR) techniques. Four wild blueberry fields were selected and completely randomized factorial (3 x 3) experiments were constructed at each site. One hundred sixty two plots (0.91 x 3 m) were made at each site, in the path of operating harvester. The total fruit yield, total losses were collected from each plot within selected fields. The harvester was operated at specific levels of ground speed (1.20, 1.60 and 2.00 km h-1) and head rotational speed (26, 28 and 30 rpm). The readings of slope, plant height, and fruit zone were also recorded from each plot. The collected data were normalized, and 70% of the data were utilized for training, and 30% for validation of the developed models using ANN and MR techniques. The developed models were validated internally and externally and the best performing model was identified based on mean square error (MSE), root mean square error (RMSE), coefficient of efficiency (CE) and coefficient of determination (R2). Results of scatter plot among the RMSE and epoch suggested that an epoch size of 15000 was appropriate to process fruit losses using ANN approach. Results revealed that the prediction accuracy of the MR models was lower (R2 = 0.46; RMSE = 0.14) than the ANN model (R2 = 0.84; RMSE = 0.075) for training dataset, which might be due to the non-linear nature of the data. Results reported that the ANN model predicted fruit losses with higher (R2 = 0.63; RMSE = 0.11) accuracy when compared with MR model (R2 = 0.37; RMSE = 0.15) for external validation dataset. Overall, the results of the study suggested that the ANN model was able to predict fruit losses accurately and reliably as functions of fruit yield, crop and machine variables. This study will help to identify the factors responsible for fruit losses and to suggest optimal harvesting scenarios to improve berry picking efficiency and recovery.