Utilising artificial intelligence for cultivating decorative plants
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...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-12-01
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| Series: | Botanical Studies |
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| Online Access: | https://doi.org/10.1186/s40529-024-00445-9 |
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| author | Nurdana Salybekova Gani Issayev Aikerim Serzhanova Valery Mikhailov |
| author_facet | Nurdana Salybekova Gani Issayev Aikerim Serzhanova Valery Mikhailov |
| author_sort | Nurdana Salybekova |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b8e5f8e43afe46ffa32409bf6b43573d |
| institution | Kabale University |
| issn | 1999-3110 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Botanical Studies |
| spelling | doaj-art-b8e5f8e43afe46ffa32409bf6b43573d2024-12-22T12:22:25ZengSpringerOpenBotanical Studies1999-31102024-12-0165111010.1186/s40529-024-00445-9Utilising artificial intelligence for cultivating decorative plantsNurdana Salybekova0Gani Issayev1Aikerim Serzhanova2Valery Mikhailov3Department of Biology, Khoja Akhmet Yassawi International Kazakh-Turkish UniversityDepartment of Biology, Khoja Akhmet Yassawi International Kazakh-Turkish UniversityDepartment of Biology, Khoja Akhmet Yassawi International Kazakh-Turkish UniversityDepartment of System Analysis and Information Technologies, Kazan Privolzhsky Federal UniversityAbstract 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.https://doi.org/10.1186/s40529-024-00445-9ANFISArtificial neural networksDecision-makingIntegrated pest managementRisk assessmentTulips |
| spellingShingle | Nurdana Salybekova Gani Issayev Aikerim Serzhanova Valery Mikhailov Utilising artificial intelligence for cultivating decorative plants Botanical Studies ANFIS Artificial neural networks Decision-making Integrated pest management Risk assessment Tulips |
| title | Utilising artificial intelligence for cultivating decorative plants |
| title_full | Utilising artificial intelligence for cultivating decorative plants |
| title_fullStr | Utilising artificial intelligence for cultivating decorative plants |
| title_full_unstemmed | Utilising artificial intelligence for cultivating decorative plants |
| title_short | Utilising artificial intelligence for cultivating decorative plants |
| title_sort | utilising artificial intelligence for cultivating decorative plants |
| topic | ANFIS Artificial neural networks Decision-making Integrated pest management Risk assessment Tulips |
| url | https://doi.org/10.1186/s40529-024-00445-9 |
| work_keys_str_mv | AT nurdanasalybekova utilisingartificialintelligenceforcultivatingdecorativeplants AT ganiissayev utilisingartificialintelligenceforcultivatingdecorativeplants AT aikerimserzhanova utilisingartificialintelligenceforcultivatingdecorativeplants AT valerymikhailov utilisingartificialintelligenceforcultivatingdecorativeplants |