An Automated Platform for Habituation Analysis in Mimosa Pudica Using an Automated Habituation Assessment (AHA) Algorithm

This work presents a low-cost automated system for studying habituation, a form of non-associative learning, in the Mimosa pudica plant. The present work directly addresses these limitations of manual experimental methods, which are often inconsistent and prone to subjective bias. Our platform integrates a Raspberry Pi with a robotic stimulus and a computer vision module, all controlled by a novel Automated Habituation Assessment (AHA) algorithm. This algorithm automates the entire experimental cycle, from stimulus application to response measurement and habituation detection. The vision module has been validated, achieving 96.5 % accuracy. The complete system was tested across three independent Mimosa pudica specimens, consistently observing habituation after an average of five touches. A subsequent ANOVA test revealed no statistically significant difference in responses between the plants, confirming the method's high reproducibility. The AHA framework provides a tangible, quantifiable, and open-source solution for automated behavioral analysis, with significant implications for the study of learning in non-neural organisms.

• Publication year

  2026

• Venue

  2026 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)

• Publication date

  2026-01-22

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/IITCEE67948.2026.11394635
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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