Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches
The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr) plant growth have been analyzed...
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| Format: | Article |
| Language: | English |
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ITB Journal Publisher
2013-07-01
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| Series: | Journal of Mathematical and Fundamental Sciences |
| Online Access: | https://journals.itb.ac.id/index.php/jmfs/article/view/54 |
| _version_ | 1818330804890107904 |
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| author | Atris Suyantohadi Mochamad Hariadi Mauridhi Hery Purnomo |
| author_facet | Atris Suyantohadi Mochamad Hariadi Mauridhi Hery Purnomo |
| author_sort | Atris Suyantohadi |
| collection | DOAJ |
| description | The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr) plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN) and Lindenmayer System (L-System) methods. Research aimed to design and to visualize plant growth modeling on the soybean varieties which these could help for studying botany of plant based on fertilizer compositions on plant growth with Nitrogen (N), Phosphor (P) and Potassium (K). The soybean plant growth has been analyzed based on the treatments of plant fertilizer compositions in the experimental research to develop plant growth modeling. By using N, P, K fertilizer compositions, its capable result on the highest production 2.074 tons/hectares. Using these models, the simulation on artificial life for describing identification and visualization on the characteristic of soybean plant growth could be demonstrated and applied. |
| first_indexed | 2024-12-13T13:09:47Z |
| format | Article |
| id | doaj.art-550a0dd29bd74cf7b3fd9cb7e71730ac |
| institution | Directory Open Access Journal |
| issn | 2337-5760 2338-5510 |
| language | English |
| last_indexed | 2024-12-13T13:09:47Z |
| publishDate | 2013-07-01 |
| publisher | ITB Journal Publisher |
| record_format | Article |
| series | Journal of Mathematical and Fundamental Sciences |
| spelling | doaj.art-550a0dd29bd74cf7b3fd9cb7e71730ac2022-12-21T23:44:44ZengITB Journal PublisherJournal of Mathematical and Fundamental Sciences2337-57602338-55102013-07-0142110.5614/itbj.sci.2010.42.1.3Artificial Life of Soybean Plant Growth Modeling Using Intelligence ApproachesAtris Suyantohadi0Mochamad Hariadi1Mauridhi Hery Purnomo21Agricultural Technology Faculty, University of Gadjah Mada (UGM), Sosioyustisia, Bulaksumur, Yogyakarta, Indonesia 2Electrical Engineering Department, Industrial Technology Faculty, Institute Technology Sepuluh November (ITS), Surabaya, Indonesia2Electrical Engineering Department, Industrial Technology Faculty, Institute Technology Sepuluh November (ITS), Surabaya, Indonesia2Electrical Engineering Department, Industrial Technology Faculty, Institute Technology Sepuluh November (ITS), Surabaya, IndonesiaThe natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr) plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN) and Lindenmayer System (L-System) methods. Research aimed to design and to visualize plant growth modeling on the soybean varieties which these could help for studying botany of plant based on fertilizer compositions on plant growth with Nitrogen (N), Phosphor (P) and Potassium (K). The soybean plant growth has been analyzed based on the treatments of plant fertilizer compositions in the experimental research to develop plant growth modeling. By using N, P, K fertilizer compositions, its capable result on the highest production 2.074 tons/hectares. Using these models, the simulation on artificial life for describing identification and visualization on the characteristic of soybean plant growth could be demonstrated and applied.https://journals.itb.ac.id/index.php/jmfs/article/view/54 |
| spellingShingle | Atris Suyantohadi Mochamad Hariadi Mauridhi Hery Purnomo Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches Journal of Mathematical and Fundamental Sciences |
| title | Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches |
| title_full | Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches |
| title_fullStr | Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches |
| title_full_unstemmed | Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches |
| title_short | Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches |
| title_sort | artificial life of soybean plant growth modeling using intelligence approaches |
| url | https://journals.itb.ac.id/index.php/jmfs/article/view/54 |
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