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Memcapacitor-Based Insect Feeding Behaviour Classification With Reservoir Computing
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DOI：10.1109/JEDS.2026.3661727.png)
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Abstract
En 中文
In this work, a reservoir computing (RC) system implemented with an organic memcapacitor is presented, tailored for energy-efficient time-series classification. The neuromorphic properties of the memory device are shown, and by exploiting them we demonstrate its suitability as a physical reservoir. As a case of study, we implement a system for classification of electrical penetration graph (EPG) signals, which encode the feeding behavior of insects on plant tissues-a critical measure in agricultural pest monitoring. For this task, an accuracy over 93 % is obtained for a reservoir containing eight devices, with an average energy per pulse of 82 pJ per device, showcasing both its high performance and low energy requirement.
Keywords:
Depression
Capacitance
Neuromorphics
Voltage measurement
Reservoir computing
Programming
Hysteresis
Cathodes
Training
Semiconductor device measurement
Memcapacitor
pinmos
reservoir
computing
organic
neuromorphic
Journal
IF:
2.4
Papers:
34
Citations:
3.1K