Abstract

Quality assessment of potatoes necessitates a computer aided interpretation technique that uses a nonhazardous and noncontact, Non-Destructive Testing technique for image acquisition and a feature extraction technique for extracting and representing the features and a classifier for performing classification/grading of potatoes. In this paper, InfraRed Thermography is used for image acquisition and Convolutional Neural Networks (CNNs) are used for the classification of potatoes. In this work, thermograms of Normal, Fungus affected potatoes, potatoes with Holes and worst affected potatoes are used. Feasibility of VGGNET, RESNET and modified ALEXNET for classification of potatoes is studied. Performance is measured in terms of sensitivity and accuracy. It is found that RESNET18 outperformed all the other networks in terms of accuracy and it is the only network that classified all the affected potatoes as affected potatoes, i.e. the affected potatoes are not wrongly identified as normal.

中文翻译：

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常规CNN架构和改进的ALEXNET通过热成像对马铃薯进行分类的性能评估

摘要

马铃薯的质量评估需要一种计算机辅助解释技术，该技术需要使用非危险性和非接触式，无损检测技术进行图像采集，并使用特征提取技术来提取和表示特征，并使用分类器进行马铃薯的分类/分级。在本文中，红外热成像技术用于图像采集，而卷积神经网络（CNN）用于马铃薯的分类。在这项工作中，使用了正常，真菌感染的马铃薯，带孔马铃薯和受影响最严重的马铃薯的温度记录图。研究了VGGNET，RESNET和改进的ALEXNET在马铃薯分类中的可行性。性能是根据灵敏度和准确性来衡量的。