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Development of a Predictive Model for Wild Blueberry Harvester Fruit Losses during Harvesting Using Artificial Neural Network

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Applied Engineering in Agriculture. 32(6): 725-738. (doi: 10.13031/aea.32.10872) @2016
Authors:   Aitazaz Ahsan Farooque, Qamar Uz Zaman, Tri Nguyen-Quang, Dominic Groulx, Arnold Walter Schumann, Young K Chang
Keywords:   Artificial neural network, Crop characteristics, Fruit losses, Ground speed, Head revolutions and wild blueberry, Mechanical harvesting, Modeling of yield losses.

Abstract.

Wild blueberry is one of the most important fruit crops of Canada that produces more than 50% of the world‘s blueberries. 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 article suggested a modeling approach for prediction of fruit losses during harvesting using artificial neural network (ANN) and multiple regression (MR) techniques. Four wild blueberry sites were selected and completely randomized factorial (3 x 3) experiments were conducted at each site. One hundred sixty-two plots (0.91 x 3 m) were made at each site, in the path of operating harvester. Total fruit yield and losses were collected from each plot within selected sites. 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 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 calibration, and 30% for validation of developed models using ANN and MR techniques. Results of root mean square error (RMSE) suggested that the tanh-sigmoid transfer function between the hidden layer and output layer was the best fit for this study. The developed models were validated internally and externally and the best performing configurations were identified based on RMSE, coefficient of efficiency, percent variation, and coefficient of determination. Results of scatter plot among the RMSE and epoch suggested that an epoch size (iterative steps) of 15,000 was appropriate to predict fruit losses using ANN approach. Results revealed that the prediction accuracy of MR model was lower (R2 = 0.46; RMSE = 0.14%) than the ANN model (R2 = 0.84; RMSE = 0.075%) for calibration dataset. 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, results of this study suggested that the ANN model was able to accurately and reliably predict fruit losses during harvesting. These results can help to identify the factors responsible for fruit losses and to suggest optimal harvesting scenarios to improve harvesting efficiency.

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