Determination of the effect of GA3 applications on plant development, nutrient content change and analysis of root architectural features using ML artificial neural network modelling in Tulipa saxatilis
This study has determined the effects of gibberallic acid (GA3) on the plant development, root and bulb nutrient content in the wild species Tulipa saxatilis. Spray treatments of GA3 at 0, 100, 200 and 400 ppm wereconducted in research. 400 ppm is the most effective application, which increased plan...
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2024-10-01
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| Series: | Folia Horticulturae |
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| Online Access: | https://doi.org/10.2478/fhort-2024-0024 |
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| author | Sari Ömer |
| author_facet | Sari Ömer |
| author_sort | Sari Ömer |
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| description | This study has determined the effects of gibberallic acid (GA3) on the plant development, root and bulb nutrient content in the wild species Tulipa saxatilis. Spray treatments of GA3 at 0, 100, 200 and 400 ppm wereconducted in research. 400 ppm is the most effective application, which increased plant height and flower stem length by 39% and 35.6%, respectively. On the other hand, the highest results in flower number were achieved in the control (2). Vase life and number of bulblets were 42% and 42.9% higher, respectively, at 100 ppm than in the control. Also, 200 ppm was the best application to increase mother bulb weight and diameter by 117.1% and 21.4%, respectively. Of bulb were found only N and P contents to be lower than the control; most effective application was 100 ppm for K, Fe, Cu and Mn content; 200 ppm for Ca and Zn content; and 400 ppm for Mn content. Although GA3 applications had different effects on root architecture, 200 ppm was generally the most effective. As a result, an increase in plant height, bulb characteristics, bulb number and vase life was achieved in T. saxatilis, which has a relatively small stem length. Additionally, the study employed machine learning algorithms, including LR, MCC, MLP, J48, PART and Baggin. The input variables were assessed to model and predict the root traits. Performance percentages of ML algorithms were listed as LR > MCC > MLP > J48 > PART > Baggin. |
| format | Article |
| id | doaj-art-6a2b7ebc764046eca12182df3b13a9aa |
| institution | Kabale University |
| issn | 2083-5965 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Sciendo |
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| series | Folia Horticulturae |
| spelling | doaj-art-6a2b7ebc764046eca12182df3b13a9aa2025-01-20T11:09:42ZengSciendoFolia Horticulturae2083-59652024-10-0136338139810.2478/fhort-2024-0024Determination of the effect of GA3 applications on plant development, nutrient content change and analysis of root architectural features using ML artificial neural network modelling in Tulipa saxatilisSari Ömer0Department of Horticulture, Black Sea Agricultural Research Institute, Samsun, TurkeyThis study has determined the effects of gibberallic acid (GA3) on the plant development, root and bulb nutrient content in the wild species Tulipa saxatilis. Spray treatments of GA3 at 0, 100, 200 and 400 ppm wereconducted in research. 400 ppm is the most effective application, which increased plant height and flower stem length by 39% and 35.6%, respectively. On the other hand, the highest results in flower number were achieved in the control (2). Vase life and number of bulblets were 42% and 42.9% higher, respectively, at 100 ppm than in the control. Also, 200 ppm was the best application to increase mother bulb weight and diameter by 117.1% and 21.4%, respectively. Of bulb were found only N and P contents to be lower than the control; most effective application was 100 ppm for K, Fe, Cu and Mn content; 200 ppm for Ca and Zn content; and 400 ppm for Mn content. Although GA3 applications had different effects on root architecture, 200 ppm was generally the most effective. As a result, an increase in plant height, bulb characteristics, bulb number and vase life was achieved in T. saxatilis, which has a relatively small stem length. Additionally, the study employed machine learning algorithms, including LR, MCC, MLP, J48, PART and Baggin. The input variables were assessed to model and predict the root traits. Performance percentages of ML algorithms were listed as LR > MCC > MLP > J48 > PART > Baggin.https://doi.org/10.2478/fhort-2024-0024flower stem lengthga3growingmachine learningnutrient contentroot analysingtulips |
| spellingShingle | Sari Ömer Determination of the effect of GA3 applications on plant development, nutrient content change and analysis of root architectural features using ML artificial neural network modelling in Tulipa saxatilis Folia Horticulturae flower stem length ga3 growing machine learning nutrient content root analysing tulips |
| title | Determination of the effect of GA3 applications on plant development, nutrient content change and analysis of root architectural features using ML artificial neural network modelling in Tulipa saxatilis |
| title_full | Determination of the effect of GA3 applications on plant development, nutrient content change and analysis of root architectural features using ML artificial neural network modelling in Tulipa saxatilis |
| title_fullStr | Determination of the effect of GA3 applications on plant development, nutrient content change and analysis of root architectural features using ML artificial neural network modelling in Tulipa saxatilis |
| title_full_unstemmed | Determination of the effect of GA3 applications on plant development, nutrient content change and analysis of root architectural features using ML artificial neural network modelling in Tulipa saxatilis |
| title_short | Determination of the effect of GA3 applications on plant development, nutrient content change and analysis of root architectural features using ML artificial neural network modelling in Tulipa saxatilis |
| title_sort | determination of the effect of ga3 applications on plant development nutrient content change and analysis of root architectural features using ml artificial neural network modelling in tulipa saxatilis |
| topic | flower stem length ga3 growing machine learning nutrient content root analysing tulips |
| url | https://doi.org/10.2478/fhort-2024-0024 |
| work_keys_str_mv | AT sariomer determinationoftheeffectofga3applicationsonplantdevelopmentnutrientcontentchangeandanalysisofrootarchitecturalfeaturesusingmlartificialneuralnetworkmodellingintulipasaxatilis |