Applications - Precision agriculture

Descripción

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.

Publicaciones

• 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