基于图像处理技术的香蕉新鲜度鉴定

Yanusha Mehendran, T. Kartheeswaran, N. Kodikara
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引用次数: 4

摘要

香蕉提供快速的能量,是一种世界范围的水果。香蕉全年都有,很少引起健康问题。香蕉是斯里兰卡最重要的水果之一,因为它被广泛消费,适用于所有情况。香蕉无疑是健康的,而且有出口价值。因此,确定新鲜度对确保产品质量和市场价值至关重要。以天数衡量香蕉新鲜度的传统方法需要经验丰富的专家进行肉眼检查。由于专家并不总是可用的,我们开发了一种方法来确定香蕉的新鲜度使用图像处理技术。在这项调查中,使用高质量的移动相机获得了不同水平的香蕉图像。利用K-Means聚类识别香蕉感兴趣的区域,并利用支持向量机(SVM)模型根据输入图像中选择的特征进行训练,估计香蕉的新鲜度。本研究的评估研究了几个特征组合,特征之间的关系Energy, Contrast, Correlation, RMS, homogenous, Mean, Standard deviation, Entropy, Greenness, Kurtosis, Skewness和Variance的准确度为81.75%。该系统的目标是激励未来的研究人员将这种方法转化为一种移动应用程序,该应用程序还结合了人工智能(AI),用于自主批量观测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Banana Freshness Identification Using Image Processing Techniques
Bananas provide rapid energy and are a worldwide available fruit. Bananas are also available all year and seldom cause health problems. Banana is one of the most significant fruits in Sri Lanka since it is extensively consumed and suitable for all situations. Bananas are undoubtedly healthful and have export worth. As a result, determining freshness is critical to ensuring product quality and market value. The conventional method for measuring the freshness of a banana in terms of days necessitates the naked eye inspection of experienced specialists. Because specialists are not always available, we developed a method for determining the freshness of bananas using image processing techniques. For this investigation, images of bananas at various levels were obtained using a high-quality mobile camera. K-Means clustering was used to identify the interesting region of the bananas, and a Support Vector Machine (SVM) model was utilized to estimate freshness by training using the chosen features from the input images. Several feature combinations were investigated for this study’s evaluation, and the relationship between the features Energy, Contrast, Correlation, RMS, Homogeneity, Mean, Standard deviation, Entropy, Greenness, Kurtosis, Skewness, and Variance yielded an accuracy of 81.75 percent. This system’s goal is to inspire future researchers to turn this method into a mobile application that also incorporates Artificial Intelligence (AI) for autonomous bulk observation.
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