Applications - Precision agriculture
Identificación de rodales de mala hierba con técnicas de teledetección, clasificación de cubiertas en cultivos.
- Collaborator Institution: Spanish Inter-Ministerial Commision of Science and Technology, European Regional Development fund and Spanish Ministry of Education and Science. Institute for Sustainable Agriculture, CSIC, Spain.
- Main targeted goal: Avoid the soil erosion with cover crops between rows, control strategies for herbicides, to address the presence or absence of cover crops, detection of weed infested fields, Yield prediction.
- Methodological subfield: Supervised Classification, Logistic Regression, Artificial Neural Networks, Evolutionary Computation, Metaheuristics, Decision-making process, agricultural remote sensing, crop parameters in precision farming.
- Methodological contribution: New classification and regresion algorithms, new methods to analyze multispectral imagery, new artificial neural netwoks models.
- Impact in domain field: New models for stimating parameters in precision farming, more accuracy in precision agriculture for economic savings and production increases.
- Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery
- A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method
- Object-Based Image Classification of Summer Crops with Machine Learning Methods
- A multi-objective neural network based method for cover crop identification from remote sensed data
- A logistic radial basis function regression method for discrimination of cover crops in olive orchards
- Structural simplification of hybrid neuro-logistic regression models in multispectral analysis of remote sensed data
- Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data
- Mapping sunflower yield as affected by Ridolfia segetum patches and elevation by applying Evolutionary Product Unit Neural Networks to remote sensed data
- Predicción de mapas de cosecha de girasol infestado de Ridolfia segetum en imágenes remotas mediante Redes Neuronales Evolutivas de Unidad Producto