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首页> 外文会议>Annual International Meeting of the American Society of Agricultural and Biological Engineers >Blueberry fruit detection by Bayesian classifier and support vector machine based on visible to near-infrared multispectral imaging
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Blueberry fruit detection by Bayesian classifier and support vector machine based on visible to near-infrared multispectral imaging

机译：Blueberry果实检测Bayesian分类器和支持向量机基于可见的近红外多光谱成像

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Early yield estimation based on computer vision enables better labor deployment and lower harvest expense in a large scale blueberry field. In this study, ninety multispectral images with near-infrared (NIR), red(R) and green(G) bands were collected from southern Highbush blueberry variety 'Sweetcrisp' from a commercial blueberry field in Waldo, Florida, during 20 April 2011 and 15 May 2011. Five thousand pure fruit pixels and 5000 background pixels were collected from the images. 66% of them were ina calibration set and the other 34% were used as a validation set. Various representations of the multispectral color models (MHSI, MYIQ, MYCbCr) originated from the NIR-R-G color model were used as the features. Bayesian classifier and support vector machine were applied for the classification of the fruit and background classes. Principle component analysis was applied before Bayesian classification for the optimized use of the features. Results show that support vector machine outperformed the Bayesian classifier with higher true positive rate (84% for fruit class and 73% for background class) and lower false positive rate (27% for fruit class and 16% for background class) in the fruit/background classification. In addition, 1000 pixels of each ofeight classes (mature fruit, mid-mature fruit, young fruit, leaf, branch, soil, sky and reference board, which were found in most images) were also classified by using the two classification techniques. The true positive rates for mid-mature fruit and young fruit class were around 50%, which indicates that the color spaces were not useful for the classification of different fruit stages.
机译：基于计算机愿景的早期收益率估算使大规模蓝莓领域的劳动力部署和更低的收获费用。在这项研究中，从佛罗里达州沃尔达（Waldo）的商业蓝莓领域，从佛罗里达州Waldo，2011年4月20日，从南部高博乌什蓝莓品种“甜点”中收集九十多光谱图像。 2011年5月15日。从图像中收集五千纯果子像素和5000个背景像素。 66％的其中66％是INA校准组，其他34％用作验证集。使用来自NIR-R-G颜色模型的多光谱颜色模型（MHSI，MYIQ，MYCBCR）的各种表示作为特征。贝叶斯分类器和支持向量机应用于水果和背景课程的分类。在贝叶斯分类之前应用原理分量分析，以优化的特征。结果表明，支持向量机表现出贝叶斯分类器的表现优于较高的真实阳性率（果实类别84％，背景课程为73％），水果中的较低误率（果实课程27％，27％）/背景分类。此外，每种级别的1000像素（成熟的水果，中成熟的水果，年轻水果，叶子，分支，土壤，天空和参考板）也通过使用两种分类技术进行分类。成熟水果和幼级班级的真正阳性率为50％，这表明颜色空间对不同果阶段的分类无用。

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