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Improved Quality Detection Technique for Fruits Using GLCM and MultiClass SVM

机译:使用GLCM和多标准SVM改进水果质量检测技术

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Intake of healthy fruits and vegetables is vital as they are the source of energy for all living beings. There is an increasing demand for quality in all the consumed food items. Nowadays, starting from consumers, retailers to food manufacturing companies are inspecting food visually for its quality. This manual process incurs more time and it is a laborious and tiring task. So, there is a demand for an automated process which quickly examines, detects the defects and sorts them according to quality. There are many factors such as temperature, humidity etc., affect the quality of fruits. In this work, we have put forward a reliable mechanism for detecting the defects in fruits. The principal goal of this work is to detect and segregate low and best quality fruits. It is achieved using the combination of hardware and image processing techniques and machine learning algorithms. The novelty in this work is interfacing Raspberry Pi with MATLAB and image is captured. The segmentation, feature extraction, and classification is done using MATLAB. Our proposed system exhibits better performance than the existing system.
机译:摄入健康的水果和蔬菜是至关重要的,因为它们是所有生物的能量来源。在所有消费的食品中对质量的需求越来越大。如今,从消费者开始,食品制造公司的零售商正在视觉检查食物以获得其质量。本手册流程更多的时间,这是一个费力和累人的任务。因此,需要一种快速检查的自动化过程,检测缺陷并根据质量对其进行排序。有许多因素如温度,湿度等,影响水果的质量。在这项工作中,我们提出了一种可靠的机制来检测水果中的缺陷。这项工作的主要目标是检测和隔离低位和最优质的水果。使用硬件和图像处理技术和机器学习算法的组合实现了它。这项工作中的新颖性正在与MATLAB和图像进行覆盆子PI。使用MATLAB进行分割,特征提取和分类。我们所提出的系统表现出比现有系统更好的性能。

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