Intelligent Mango Fruit Grade Classification Using AlexNet-SPP With Mask R-CNN-Based Segmentation Algorithm

Jui-Feng Yeh;Kuei-Mei Lin;Chen-Yu Lin;Jen-Chun Kang
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引用次数: 3

Abstract

In this article, the grades of mangoes were classified using an AlexNet–spatial pyramid pooling network (SPP-Net) with a segmentation algorithm based on a Mask region-based convolutional neural network (R-CNN). Computer vision technologies have begun to be used for fruit grade classification, and this is a major topic of interest in agricultural automation. However, because insufficient fruit grade classification accuracy is achieved with these technologies, manual processing must be performed. The accuracy of fruit grade classification can be enhanced using a Mask R-CNN, SPP-Net, and specific background processing. The designed mango grade classification system contains four modules: 1) a user interface module, 2) an object detection module, 3) an image preprocessing module, and 4) a fruit grade classification module. A camera is used to capture images of mangoes for display on the user interface. The object segmentation module generates a mango shape mask and bounding box by using a Mask R-CNN. The image preprocessing module uses the generated bounding box and mango shape mask to crop the mango and color the background blue. Finally, AlexNet–SPP-Net outputs the fruit grade. We validated the proposed approach by implementing it in mango grade classification and comparing its accuracy with that of relevant existing methods from the literature. According to the experimental results, the proposed approach outperforms the traditional AlexNet-based approach.
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基于掩码R-CNN-的AlexNet SPP芒果分级智能分割算法
在本文中,使用AlexNet–空间金字塔池网络(SPP-Net)和基于Mask区域的卷积神经网络(R-CNN)的分割算法对芒果的等级进行了分类。计算机视觉技术已开始用于水果等级分类,这是农业自动化领域的一个主要研究课题。然而,由于这些技术无法达到足够的水果等级分类精度,因此必须进行手动处理。使用Mask R-CNN、SPP-Net和特定的背景处理可以提高水果等级分类的准确性。设计的芒果等级分类系统包括四个模块:1)用户界面模块,2)物体检测模块,3)图像预处理模块,4)水果等级分类模块。相机用于捕捉芒果的图像以显示在用户界面上。对象分割模块使用mask R-CNN生成芒果形状的掩码和边界框。图像预处理模块使用生成的边界框和芒果形状遮罩来裁剪芒果并将背景染成蓝色。最后,AlexNet–SPP-Net输出水果等级。我们通过在芒果等级分类中实施该方法,并将其准确性与文献中现有的相关方法进行比较,验证了所提出的方法。根据实验结果,所提出的方法优于传统的基于AlexNet的方法。
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2024 Index IEEE Transactions on AgriFood Electronics Vol. 2 Table of Contents Front Cover IEEE Circuits and Systems Society Information IEEE Circuits and Systems Society Information
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