Performance Evaluation of Conventional CNN Architectures and Modified ALEXNET for the Classification of Potatoes by Thermal Imaging
Source abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Chemical Sensor Technologies · Spectroscopy and Chemometric Analyses · Identification and Quantification in Food
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (10)
Show source-provided references
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