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International Journal of Food Engineering

Editor-in-Chief: Chen, Xiao Dong

IMPACT FACTOR increased in 2015: 0.712

SCImago Journal Rank (SJR) 2015: 0.298
Source Normalized Impact per Paper (SNIP) 2015: 0.398
Impact per Publication (IPP) 2015: 0.738

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Optimizing Machine Vision Based Applications in Agricultural Products by Artificial Neural Network

Jason Liu1 / Weihua Wade Yang2 / Yongsheng Wang3 / Taha M. Rababah4 / Lloyd T. Walker5

1Cornell University

2University of Florida

3Alabama A&M University

4Jordan University of Science and Technology

5Alabama A&M University

Citation Information: International Journal of Food Engineering. Volume 7, Issue 3, ISSN (Online) 1556-3758, DOI: https://doi.org/10.2202/1556-3758.1745, May 2011

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The use of trained artificial neural networks (ANNs) for agricultural processing, handling, and process control, such as pattern recognition, classification and weight prediction, offers potential for multi-dimensional function fittings and enhanced accuracy in machine-vision based procedures. In this study, optimization of ANNs for machine vision based applications for better prediction accuracy has been conducted using soybean weighing as an example. Neural network systems consisting of a varying number of neurons trained under dissimilar algorithms were compared in determining the weights of soybeans based on the correlation of weight to features extracted from one- and two-direction images. Results show that imaging from the side of a soybean produces superior data to that of top-down images, and that with a properly trained neural network, weight predictions could be accurate up to a relative error of less than three percent. Furthermore, the continuous dependence of weight to features of the soybean suggested use of a training batch consisting of uniformly distributed weights.

Keywords: artificial neural network; weighing; machine vision; image processing; agricultural product; soybean; continuity

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