Knowledge-based systems in agriculture
Knowledge-based systems in agriculture
An application of artificial neural networks in environmental pollution forecasting
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Comparing statistical and neural network approaches for urban air pollution time series analysis
MIC '08 Proceedings of the 27th IASTED International Conference on Modelling, Identification and Control
A fuzzy logic based system for heavy metals loaded wastewaters monitoring
CI'10 Proceedings of the 4th WSEAS international conference on Computational intelligence
Applying artificial neural networks in environmental prediction systems
ICAI'10 Proceedings of the 11th WSEAS international conference on Automation & information
The neural network-based forecasting in environmental systems
WSEAS Transactions on Systems and Control
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This paper investigates the feed forward neural networks utilization in improving some crop growth parameters (e.g. Leaf Area Index) forecasting based on cumulated Photosynthetically Active Radiation (PAR) time series. PAR data recorded by the DAQ system were used as input in a nonlinear sigmoid function with 3 parameters. LAI series of the red clover canopies represented the outputs of the nonlinear identified model. These outputs (N=257 for each set) were used to train different network topologies of feed-forward ANN using Quickprop and Rprop learning algorithms. The ANN output represented the one LAI ahead forecasted value. Best fitting results were obtained using QuickProp algorithm with 6 units in the input layer, 4 or 8 neurons in the hidden layer and one output neuron for unfertilized variants, and 10-4-1 and 6-8-1 network topologies for foliar fertilized variants. Advantages of neural computing techniques relied on faster computation, learning ability and noise rejection.