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Evaluation of the effect of moisture content on cereal grains by digital image analysis

机译：通过数字图像分析评估水分对谷物的影响

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摘要

Physical appearance and kernel morphology significantly affect the grade of a harvested crop in addition to other factors such as test weight, percentage of foreign matter and constituent components. Moisture content of grain can potentially affect the physical appearance and kernel morphology. The objective of this study was to evaluate the effect of moisture content on the classification capability of colour, morphology and textural features of imaged grains. Colour images of individual kernels and bulk samples of three grain types, namely Canada Western Amber Durum (CWAD) wheat, Canada Western Red Spring (CWRS) wheat and barley were acquired using a machine vision system. The grain kernels were conditioned to 12%, 14%, 16%, 18% and 20% moisture contents before imaging. Previously developed algorithms were used to extract 123 colour, 56 textural features from bulk sample images and 123 colour, 56 textural, 51 morphological features from individual kernel images. The extracted features were analysed for the effect of moisture content. Statistical classifiers and a back propagation neural network model were used for classifying the grain bulk at different moisture contents. The colour and textural features of bulk grain images were affected by the moisture content more than that of the single kernel images.
机译：物理外观和籽粒形态除其他因素（例如容重，异物百分比和组成成分）外，还会显着影响收获作物的等级。谷物中的水分含量可能会影响其物理外观和籽粒形态。这项研究的目的是评估水分含量对成像颗粒的颜色，形态和纹理特征的分类能力的影响。使用机器视觉系统获取了三种谷物类型的单个籽粒和大量样品的彩色图像，即加拿大西部琥珀色杜伦麦（CWAD）小麦，加拿大西部红春小麦（CWRS）和大麦。在成像之前，将谷粒的水分含量调节为12％，14％，16％，18％和20％。使用以前开发的算法从大量样本图像中提取123色，56个纹理特征，并从单个内核图像中提取123色，56个纹理，51个形态特征。分析提取的特征的水分含量的影响。使用统计分类器和反向传播神经网络模型对不同含水量的谷物进行分类。水分含量比单粒图像受水分含量影响更大。

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