Classification of cereal grains using a flatbed scanner

Classification of cereal grains using a flatbed scanner
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使用平板扫描仪对谷物进行分类

DOI：
10.13031/2013.15408
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
M. Jayas
M. Jayas
中科院分区：
--
文献类型：
--
作者：
J. Paliwal;M. Jayas

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为了寻找一种廉价的机器视觉系统（MVS）来识别和分类， 谷物，使用平板扫描仪并评估其性能。散装图片 大麦、加拿大西部琥珀硬粒小麦（CWAD）的样品和单个谷粒， 获得加拿大西部红春（CWRS）小麦、燕麦和黑麦，并进行分类。 使用四层反向传播神经网络完成。分类准确率超过99% 使用一组10个颜色和纹理特征获得的散装样品。对于单个内核 图像，需要一组至少30个特征（形态，颜色和纹理）来实现 相似的分类精度。单个内核样本的分类精度在 96和99%。
In the quest for an inexpensive machine-vision system (MVS) to identify and classify cereal grains, a flatbed scanner was used and its performance was evaluated. Images of bulk samples and individual grain kernels of barley, Canada Western Amber Durum (CWAD) wheat, Canada Western Red Spring (CWRS) wheat, oats, and rye were acquired and classification was done using a four layer back-propagation neural network. Classification accuracies in excess of 99% were obtained using a set of 10 color and textural features for bulk samples. For single kernel images, a set of at least 30 features (morphological, color, and textural) was required to achieve similar classification accuracies. Classification accuracies for single kernel samples varied between 96 and 99%.