Mapping sunflower yield as affected by Ridolfia segetum patches and elevation by applying evolutionary product unit neural networks to remote sensed data
- 31 March 2008
- journal article
- Published by Elsevier BV in Computers and Electronics in Agriculture
- Vol. 60 (2), 122-132
- https://doi.org/10.1016/j.compag.2007.07.011
Abstract
No abstract availableKeywords
This publication has 46 references indexed in Scilit:
- STATISTICAL AND NEURAL METHODS FOR SITE–SPECIFIC YIELD PREDICTIONTransactions of the ASAE, 2003
- AE—Automation and Emerging TechnologiesBiosystems Engineering, 2002
- Influence of weed maturity levels on species classification using machine visionWeed Science, 2002
- Remote Sensing of Winter Wheat Tiller Density for Early Nitrogen Application DecisionsAgronomy Journal, 2001
- Estimation of Soil Physical Properties Using Remote Sensing and Artificial Neural NetworkRemote Sensing of Environment, 2000
- Remedial Correction of Yield Map DataPrecision Agriculture, 1999
- SEMAGI — an expert system for weed control decision making in sunflowersCrop Protection, 1995
- Competition between Ridolfia segetum and sunflowerWeed Research, 1995
- An evolutionary algorithm that constructs recurrent neural networksIEEE Transactions on Neural Networks, 1994
- Product Units: A Computationally Powerful and Biologically Plausible Extension to Backpropagation NetworksNeural Computation, 1989