Survey Paper on Fruit Recognition, Classification and Quality Health Maintenance

Sanketa Kulkarni, V. S. Krushnasamy
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引用次数: 4

Abstract

This research focuses on fruit and vegetables classification, recognition based on its health and quality by using Raspberry pi board, which is further integrated with digital image processing techniques and machine learning concepts. Convolutional Neural Networks (CNN) is generally used to perform image identification and categorization in the object recognition systems. The recent advancements in deep learning-based models assist in performing complex image recognition. This study also proposes an effective CNN-based method for performing fruit recognition, fruit maturity based categorization, and calorie estimation. Datasets are used to train the proposed machine learning model. The dataset used here is a combination of image data containing various types of fruit; here the proposed cost-effective yet powerful fruit quality maintenance method will be useful for fruit vendors and farmers.
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水果识别、分类与品质保健研究综述
本研究以树莓派板为基础,结合数字图像处理技术和机器学习概念,对果蔬进行健康和品质分类识别。在物体识别系统中,通常使用卷积神经网络(CNN)来进行图像识别和分类。基于深度学习的模型的最新进展有助于执行复杂的图像识别。本研究还提出了一种有效的基于cnn的水果识别、水果成熟度分类和卡路里估计方法。数据集用于训练提出的机器学习模型。这里使用的数据集是包含各种水果的图像数据的组合;在这里,提出的具有成本效益且功能强大的水果品质维护方法将对水果摊贩和农民有用。
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