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Classification and Grading of Harvested Mangoes Using Convolutional Neural Network
International Journal of Fruit Science ( IF 2.4 ) Pub Date : 2022-01-11 , DOI: 10.1080/15538362.2021.2023069
Hafiz Muhammad Rizwan Iqbal 1 , Ayesha Hakim 1
Affiliation  

ABSTRACT

Mango (Mangifera Indica L. Family Anacardiaceae) is a climatic fruit with a short shelf life. A significant percentage of fruit is wasted each year due to the time-consuming manual grading and classification process. There is a need to replace the traditional methods by adopting automation technologies in the agriculture sector. This paper presents a deep learning-based approach for automated classification and grading of eight cultivars of harvested mangoes based on quality features such as color, size, shape, and texture. Five types of data augmentation methods were used: images rotation, translation, zooming, shearing, and horizontal flip. We compared three architectures of 3-layer Convolutional Neural Network (CNN): VGG16, ResNet152, and Inception v3 on augmented data. The proposed approach achieved up to 99.2% classification accuracy and 96.7% grading accuracy respectively using the Inception v3 architecture of CNN.



中文翻译:

使用卷积神经网络对收获芒果进行分类和分级

摘要

芒果 ( Mangifera Indica L. Family Anacardiaceae) 是一种保质期短的气候水果。由于耗时的手动分级和分类过程,每年有很大一部分水果被浪费。有必要通过在农业部门采用自动化技术来取代传统方法。本文提出了一种基于深度学习的方法,用于根据颜色、大小、形状和质地等质量特征对 8 个收获芒果品种进行自动分类和分级。使用了五种类型的数据增强方法:图像旋转、平移、缩放、剪切和水平翻转。我们在增强数据上比较了 3 层卷积神经网络 (CNN) 的三种架构:VGG16、ResNet152 和 Inception v3。所提出的方法实现了高达 99.2% 的分类准确率和 96。

更新日期:2022-01-12
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