Utilising artificial intelligence for cultivating decorative plants - Nurdana Salybekova; Gani Issayev; Aikerim Serzhanova; Valery Mikhailov (Botanical Studies)
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.