Toward automated tomato harvesting system integration of haptic based piezoresistive nanocomposite and machine learning
Carbon nanotubes (CNT)/polydimethylsiloxane (PDMS) have been investigated as potential materials for tomato-harvesting applications. The current-voltage (I–V) and current time (I–t) properties, as well as tomato hardness measurement and support-vector machine learning, were used to determine the per...
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| Format: | Article |
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IEEE
2021
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| _version_ | 1796982874397736960 |
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| author | Azhari, Saman Setoguchi, Takuya Sasaki, Iwao Nakagawa, Arata Ikeda, Kengo Azhari, Alin Hasan, Intan Helina |
| author_facet | Azhari, Saman Setoguchi, Takuya Sasaki, Iwao Nakagawa, Arata Ikeda, Kengo Azhari, Alin Hasan, Intan Helina |
| author_sort | Azhari, Saman |
| collection | UPM |
| description | Carbon nanotubes (CNT)/polydimethylsiloxane (PDMS) have been investigated as potential materials for tomato-harvesting applications. The current-voltage (I–V) and current time (I–t) properties, as well as tomato hardness measurement and support-vector machine learning, were used to determine the performance of the sensor with respect to sensitivity, response time, accuracy, and detection limit of the nanocomposite. The data suggested an accurate (± 5.2%) measurement in a low-weight region of tomato. Narrowing of the I–V hysteresis curve towards a higher weight region was observed as a result of the increase in electron pathways. The fabricated sensor displayed a higher sensitivity (15 mV $/ \mu \text{m}$ ) than the commercial sensor (1 mV $/ \mu \text{m}$ ). In addition, machine learning of the resistance–displacement curve data yielded an average accuracy level of 0.67 when tested using acquired data. |
| first_indexed | 2024-03-06T10:58:15Z |
| format | Article |
| id | upm.eprints-93398 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T10:58:15Z |
| publishDate | 2021 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-933982022-11-23T04:21:31Z http://psasir.upm.edu.my/id/eprint/93398/ Toward automated tomato harvesting system integration of haptic based piezoresistive nanocomposite and machine learning Azhari, Saman Setoguchi, Takuya Sasaki, Iwao Nakagawa, Arata Ikeda, Kengo Azhari, Alin Hasan, Intan Helina Carbon nanotubes (CNT)/polydimethylsiloxane (PDMS) have been investigated as potential materials for tomato-harvesting applications. The current-voltage (I–V) and current time (I–t) properties, as well as tomato hardness measurement and support-vector machine learning, were used to determine the performance of the sensor with respect to sensitivity, response time, accuracy, and detection limit of the nanocomposite. The data suggested an accurate (± 5.2%) measurement in a low-weight region of tomato. Narrowing of the I–V hysteresis curve towards a higher weight region was observed as a result of the increase in electron pathways. The fabricated sensor displayed a higher sensitivity (15 mV $/ \mu \text{m}$ ) than the commercial sensor (1 mV $/ \mu \text{m}$ ). In addition, machine learning of the resistance–displacement curve data yielded an average accuracy level of 0.67 when tested using acquired data. IEEE 2021-12-15 Article PeerReviewed Azhari, Saman and Setoguchi, Takuya and Sasaki, Iwao and Nakagawa, Arata and Ikeda, Kengo and Azhari, Alin and Hasan, Intan Helina (2021) Toward automated tomato harvesting system integration of haptic based piezoresistive nanocomposite and machine learning. IEEE Sensors Journal, 21 (24). 27810 - 27817. ISSN 1558-1748 https://ieeexplore.ieee.org/document/9598893 10.1109/JSEN.2021.3124914 |
| spellingShingle | Azhari, Saman Setoguchi, Takuya Sasaki, Iwao Nakagawa, Arata Ikeda, Kengo Azhari, Alin Hasan, Intan Helina Toward automated tomato harvesting system integration of haptic based piezoresistive nanocomposite and machine learning |
| title | Toward automated tomato harvesting system integration of haptic based piezoresistive nanocomposite and machine learning |
| title_full | Toward automated tomato harvesting system integration of haptic based piezoresistive nanocomposite and machine learning |
| title_fullStr | Toward automated tomato harvesting system integration of haptic based piezoresistive nanocomposite and machine learning |
| title_full_unstemmed | Toward automated tomato harvesting system integration of haptic based piezoresistive nanocomposite and machine learning |
| title_short | Toward automated tomato harvesting system integration of haptic based piezoresistive nanocomposite and machine learning |
| title_sort | toward automated tomato harvesting system integration of haptic based piezoresistive nanocomposite and machine learning |
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