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Utilising artificial intelligence for cultivating decorative plants. | LitMetric

Utilising artificial intelligence for cultivating decorative plants.

Bot Stud

Department of System Analysis and Information Technologies, Kazan Privolzhsky Federal University, Kazan, Russian Federation.

Published: December 2024

AI Article Synopsis

  • The research evaluates how effective artificial intelligence models are at forecasting risk levels in tulip greenhouses, focusing on different tulip varieties in the Almaty region in 2022.
  • A comparison was made between two groups of greenhouses: one using standard monitoring methods and the other using AI, particularly the "Expert-Pro" recurrent neural network, which showed a significant reduction in risk levels.
  • The study concludes that the "Expert-Pro" model's high accuracy suggests it can improve both crop productivity and risk management in greenhouse settings, emphasizing the value of AI in agriculture.

Article Abstract

Background: The research aims to assess the effectiveness of artificial intelligence models in predicting the risk level in tulip greenhouses using different varieties. The study was conducted in 2022 in the Almaty region, Panfilov village.

Results: Two groups of 10 greenhouses each (area 200 m2) were compared: the control group used standard monitoring methods, while the experimental group employed AI-based monitoring. We applied ANOVA, regression analysis, Bootstrap, and correlation analysis to evaluate the impact of factors on the risk level. The results demonstrate a statistically significant reduction in the risk level in the experimental group, where artificial intelligence models were employed, especially the recurrent neural network "Expert-Pro." A comparison of different tulip varieties revealed differences in their susceptibility to risks. The results provide an opportunity for more effective risk management in greenhouse cultivation.

Conclusions: The high accuracy and sensitivity exhibited by the "Expert-Pro" model underscore its potential to enhance the productivity and resilience of crops. The research findings justify the theoretical significance of applying artificial intelligence in agriculture and its practical applicability for improving risk management efficiency in greenhouse cultivation conditions.

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Source
http://dx.doi.org/10.1186/s40529-024-00445-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655720PMC

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