摘要
一位新闻记者-机器人与机器学习的新闻编辑-每日新闻-关于人工智能的研究结果在一份新的报告中讨论。根据来自新西兰奥克兰d的新闻，NewsRx编辑，这项研究指出：“干果的外观显然会影响消费者对水果质量的感知，但这是一个微妙而微妙的特征，很难定量衡量，特别是在网上。”新闻编辑们从奥克兰大学的研究中获得了一句话:“本文描述了一种方法,结合多变量统计和机器学习,结合几种简单的策略来评估难以捉摸的质量的合适替代物。有了这样一种方便的工具,我们就可以使用图像来评估一种合适的替代物。”本研究还展示了如何改变预处理和干燥条件以优化所得产品质量。具体地说，开发了一种图像批处理方法来提取颜色(色调、饱和度和值)和形态(面积、周长、以猕猴桃片热风干燥预处理为例,验证了该方法的准确性。应用偏最小二乘法和随机森林模型对猕猴桃片干燥过程中的水分比(MR)进行了较好的预测,不用任何称重装置就可以根据外观变化准确预测猕猴桃片干燥过程中的水分比.
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news originating from Aucklan d, New Zealand, by NewsRx editors, the research stated, "The appearance of dried fruit clearly influences the consumer's perception of the quality of the produc t but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online." The news editors obtained a quote from the research from University of Auckland: "This paper describes a method that combines several simple strategies to asses s a suitable surrogate for the elusive quality using imaging, combined with mult ivariate statistics and machine learning. With such a convenient tool, this stud y also shows how one can vary the pretreatments and drying conditions to optimiz e the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (a rea, perimeter, and compactness) features. The accuracy of this method was verif ied using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture r atio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing de vice."
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