Fruit classification system based on the pressure sensor coupled with support vector machine
Food and Machinery, ISSN: 1003-5788, Vol: 39, Issue: 9, Page: 83-88
2023
- 4Citations
- 51Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations4
- Citation Indexes4
- Usage51
- Downloads38
- Abstract Views13
Article Description
Objective: To improve fruit classification accuracy while considering low computational cost and low sensor cost. Methods: A fruit classification system based on capacitive pressure sensor was proposed. The system used support vector machine algorithm with a Gaussian kernel function (GKF-SVM) to classify fruits. The capacitive pressure sensors used were made of thin copper sheets and a layer of vinyl acetate, and these sensors were fixed to the thumb and index finger of a polyamide spandex glove that simulates a robotic hand. The obtained capacitance value was expressed in the form of digital level, and the data was extracted by data processing software, and the capacitance data was processed by SVM algorithm with kernel functions to determine the category of a fruit. Results; The classification results of 11 kinds of fruits in the designed cla.s.sification system shown that the smart glove using GKF-SVM algorithm could achieve high accuracy classification of fruits, which could trade off between classification accuracy, calculation cost and low sensor cost according to actual fruit classification needs. Conclusion; The research results can be used to develop electronic systems for fruit classification to improve the fruit classification performance of classification manipulators.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85201111121&origin=inward; http://dx.doi.org/10.13652/j.spjx.1003.5788.2023.60072; https://www.ifoodmm.cn/journal/vol39/iss9/13; https://www.ifoodmm.cn/cgi/viewcontent.cgi?article=5279&context=journal; https://dx.doi.org/10.13652/j.spjx.1003.5788.2023.60072; https://www.chndoi.org/Resolution/Handler?doi=10.13652/j.spjx.1003.5788.2023.60072
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