Toward Automated Tomato Harvesting System: Integration of Haptic Based Piezoresistive Nanocomposite and Machine Learning

Saman Azhari*, Takuya Setoguchi, Iwao Sasaki, Arata Nakagawa, Kengo Ikeda, Alin Azhari, Intan Helina Hasan, Mohd Nizar Hamidon, Naoto Fukunaga, Tomohiro Shibata, Hirofumi Tanaka

*この研究の対応する著者

研究成果: Article査読

抄録

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.

本文言語English
ページ(範囲)27810-27817
ページ数8
ジャーナルIEEE Sensors Journal
21
24
DOI
出版ステータスPublished - 2021 12月 15
外部発表はい

ASJC Scopus subject areas

  • 器械工学
  • 電子工学および電気工学

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