Journal
BIOSYSTEMS ENGINEERING
Volume 178, Issue -, Pages 131-144Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.biosystemseng.2018.11.010
Keywords
Corn kernel; Automatic inspection machine; Machine vision; Food engineering; Deep neural network
Funding
- Jiangsu Overseas Research & Training Program for University Prominent Young & Middle-aged Teachers and Presidents program for this research project
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Maize inspection is an important and time-consuming task in the domain of food engineering. The human-based inspection strategy needs to be brought up to date with the rapid developments in the maize industry. In this paper, an automatic maize-inspection machine is proposed. Our proposed machine integrates several new designs in terms of both hardware and software components. First, a gravity-based dual-side camera design expands the machine's field-of-view to evaluate maize kernels more thoroughly. Second, touching kernels are pre-processed using a new k-means clustering guided-curvature method, which can improve the robustness of our machine. Next, a deep convolutional neural network, which has shown promise for application in image processing, is embedded into the system to evaluate maize kernels. In this work, the ResNet, which is a deep convolutional neural network architecture, was trained by fine-tuning with 1632 images. It achieved a 98.2% prediction accuracy for 408 test images, which outperforms existing approaches. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.
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