Modeling the soybean growth in different amount of nitrogen, phosphorus and potassium using neural network
This paper proposed a simulation model of soybean growth which is effected by major nutrient factors, nitrogen, phosphorus and potassium. A feedforward neural network is used as a basis of the modelling. The combination of different percentage of nitrogen, phosphorus, potassium, time steps and the collected height data of the soybean are used as inputs. The model can predict the height at designated time intervals, whereby the result can be visualized with L-systems.
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| Main Authors: | , , |
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| Format: | conference_item biblioteca |
| Language: | eng |
| Published: |
CIRAD-AMAP
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| Subjects: | U10 - Informatique, mathématiques et statistiques, F61 - Physiologie végétale - Nutrition, modèle de simulation, Glycine max, croissance, azote, phosphore, potassium, http://aims.fao.org/aos/agrovoc/c_24242, http://aims.fao.org/aos/agrovoc/c_3301, http://aims.fao.org/aos/agrovoc/c_3394, http://aims.fao.org/aos/agrovoc/c_5192, http://aims.fao.org/aos/agrovoc/c_5804, http://aims.fao.org/aos/agrovoc/c_6139, |
| Online Access: | http://agritrop.cirad.fr/523875/ |
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| Summary: | This paper proposed a simulation model of soybean growth which is effected by major nutrient factors, nitrogen, phosphorus and potassium. A feedforward neural network is used as a basis of the modelling. The combination of different percentage of nitrogen, phosphorus, potassium, time steps and the collected height data of the soybean are used as inputs. The model can predict the height at designated time intervals, whereby the result can be visualized with L-systems. |
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