Our scientific objectives are focused on three areas:
Development of tools for obtaining and processing spatial information on crops and their major pests, such as:
Remote Sensors / Drones
Application of new monitoring technologies and Decision Support Systems (DDS) to intelligent weed control; for example:
Long-term evaluation of site-specific weed management systems
Use of DDSs to translate information into weed management decisions
Study of factors affecting the spatial and temporal development of major crop pests using geospatial technologies in combination with environmental and agronomic data
Influence of site conditions (climate, soil) and crop development on pest problems
Distribution and spread of pest organisms and pesticide-resistant populations on a landscape scale
Review of state-of-the-art technologies used in weed monitoring.
Fernández-Quintanilla C., Peña J., Andújar D., Dorado J., Ribeiro A. & López-Granados F. (2018) Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops? Weed Research 58: 259–272.
New methodologies for the construction of three-dimensional weed and crop models using depth cameras and photogrammetry..
Andújar D., Calle M., Fernández-Quintanilla C., Ribeiro A. & Dorado J. (2018) Three-dimensional modeling of weed plants using low-cost photogrammetry. Sensors 18, 1077
Andújar D., Dorado J., Fernández-Quintanilla C. & Ribeiro A. (2016) An approach to the use of depth cameras for weed volume estimation. Sensors 16, 972
Andújar D., Ribeiro A., Fernández-Quintanilla C. & Dorado J. (2016) Using depth cameras to extract structural parameters to assess the growth state and yield of cauliflower crops. Computers and Electronics in Agriculture 122:67–73.
Development and evaluation of the full protocol for the 3D characterization of woody crops with drones and its application to various agronomic objectives.
Jiménez-Brenes, F.M., López-Granados, F., de Castro, A. I., Torres-Sánchez, J., Serrano, N., Peña, J.M. 2017. Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling. Plant Methods, 13, 55.
Torres-Sánchez, J., López-Granados, F., Serrano, N., Arquero, O., Peña, J. M. 2015. High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology. PLoS ONE, 10(6), e0130479.
Semi-automatic procedure for the regional scale monitoring of crops and their phenology using satellite images.
de Castro, A. I., Six, J., Plant, R. E., Peña, J. M. (2018). Mapping Crop Calendar Events and Phenology-Related Metrics at the Parcel Level by Object-Based Image Analysis (OBIA) of MODIS-NDVI Time-Series: A Case Study in Central California. Remote Sensing, 10(11), 1745.
Peña, J.M., Gutiérrez, P.A., Hervás-Martínez, C., Six, J., Plant, R.E., López-Granados, F. 2014. Object-Based Image Classification of Summer Crops with Machine Learning Methods. Remote Sensing, 6(6), 5019–5041.