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Related Experiment Video

Updated: Jun 4, 2025

Robotic Sensing and Stimuli Provision for Guided Plant Growth
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Utilising artificial intelligence for cultivating decorative plants.

Nurdana Salybekova1, Gani Issayev2, Aikerim Serzhanova1

  • 1Department of Biology, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkistan, Kazakhstan.

Botanical Studies
|December 18, 2024
PubMed
Summary

Artificial intelligence (AI) models significantly reduced risk levels in tulip greenhouses. The recurrent neural network "Expert-Pro" showed high accuracy, improving crop resilience and agricultural risk management.

Keywords:
ANFISArtificial neural networksDecision-makingIntegrated pest managementRisk assessmentTulips

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Data Science

Background:

  • Study conducted in 2022 in Almaty region, Panfilov village.
  • Focus on predicting risk levels in tulip greenhouses across different varieties.

Purpose of the Study:

  • To assess the effectiveness of artificial intelligence models in predicting and managing risks in tulip greenhouse cultivation.
  • To compare AI-based monitoring with standard methods for risk reduction.

Main Methods:

  • Comparison of two groups: control (standard monitoring) and experimental (AI-based monitoring).
  • Application of statistical analyses including ANOVA, regression, Bootstrap, and correlation analysis.
  • Utilized the recurrent neural network model named "Expert-Pro" for AI-based monitoring.

Main Results:

  • Statistically significant reduction in risk level observed in the experimental group using AI.
  • The "Expert-Pro" recurrent neural network demonstrated particular effectiveness.
  • Identified variations in susceptibility to risks among different tulip varieties.

Conclusions:

  • The "Expert-Pro" AI model exhibits high accuracy and sensitivity, enhancing crop productivity and resilience.
  • Findings support the theoretical significance and practical applicability of AI in agricultural risk management.
  • AI-driven monitoring offers improved efficiency for greenhouse cultivation.