Geospatial and Precision Technologies For Sustainable Agriculture (tec4AGR0) Group
The research activity of this group aims to achieve a better understanding of the factors affecting the spatial and temporal development of the major crop pests, using monitoring tools and technologies that allow obtaining and processing geospatial data (proximate and remote sensors, GIS and specific software). The joint application of new technologies and Decision Support Systems allows this information to be transferred to intelligent pest control.
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:
- Monitoring technologies based on remote sensing - drones
- Deep learning
- 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
ACCESS TO THE GROUP WEBSITE
- Group Leader
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DORADO GÓMEZ, JOSÉ
Scientific Researcher - Personnel
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FDEZ-QUINTANILLA GALLASTEGUI, CÉSAR
Ad Honorem Research ProfessorPEÑA BARRAGÁN, JOSÉ MANUEL
ResearcherNIETO SOLANA, HÉCTOR
ResearcherBORRA SERRANO, IRENE
Post-doctoral ResearcherBURCHARD LEVINE, VICENTE FELIPE
Post-doctoral ResearcherMESÍAS RUIZ, GUSTAVO ADOLFO
Pre-doctoral ResearcherMENA CASTILLO, JUAN DIEGO
Pre-doctoral ResearcherGARCÍA GUERRA, JOSÉ
TechnicianCAMPOS LÓPEZ, DAVID
Research AssistantMARTÍN FERNÁNDEZ, JOSÉ MANUEL
Research AssistantHERNÁNDEZ MIRANZO, VÍCTOR
Research Assistant
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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. https://doi.org/10.1111/wre.12307
Lati et al. (2021) Site-specific weed management – constraints and opportunities for the weed research community. Weed Res. https://doi.org/10.1111/wre.12469
Fernández-Quintanilla et al. (2020) Site-Specific Based Models. In: Decision Support Systems for Weed Management (GR Chantre, JL González-Andújar, ed.), pp. 143–157, Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-44402-0_7
Fernández-Quintanilla et al. (2018) Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops? Weed Res. 58: 259–272. https://doi.org/10.1111/wre.12307 -
Phenotyping and 3D characterization of crops with drones and its application to various agronomic objectives
Ostos-Garrido, F.J., de Castro, A.I., Torres-Sánchez, J., Pistón, F., Peña, J.M. 2019. High-Throughput Phenotyping of Bioethanol Potential in Cereals Using UAV-Based Multi-Spectral Imagery. Frontiers in Plant Science, 10, 948. https://doi.org/10.3389/fpls.2019.00948
Freeman, D., Gupta, S., Smith, D.H., Maja, J.M., Robbins, J., Owen, J.S., Peña, J.M., de Castro, A.I. 2019. Watson on the Farm: Using Cloud-Based Artificial Intelligence to Identify Early Indicators of Water Stress. Remote Sensing, 11, 2645. https://doi.org/10.3390/rs11222645
Rueda-Ayala, V.P., Peña, J.M., Höglind, M., Bengochea-Guevara, J.M., Andújar, D., 2019. Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley. Sensors, 19, 535. https://doi.org/10.3390/s19030535
Torres-Sánchez, J., de Castro, A.I., Peña, J.M., Jiménez-Brenes, F.M., Arquero, O., Lovera, M., López-Granados, F. 2018. Mapping the 3D structure of almond trees using UAV acquired photogrammetric point clouds and object-based image analysis. Biosystems Engineering, 176, 172–184. https://doi.org/10.1016/j.biosystemseng.2018.10.018 -
Artificial intelligence procedures to exploit the use of remote sensing in agriculture
• de Castro, A.I., Peña, J.M., Torres-Sánchez, J., Jiménez-Brenes, F.M., Valencia-Gredilla, F., Recasens Guinjuan, J., López-Granados, F., 2020. Mapping Cynodon Dactylon Infesting Cover Crops with an Automatic Decision Tree-OBIA Procedure and UAV Imagery for Precision Viticulture. Remote Sensing, 12, 56. https://doi.org/10.3390/rs12010056
• Freeman, D., Gupta, S., Smith, D.H., Maja, J.M., Robbins, J., Owen, J.S., Peña, J.M., de Castro, A.I., 2019. Watson on the Farm: Using Cloud-Based Artificial Intelligence to Identify Early Indicators of Water Stress. Remote Sensing, 11, 2645. https://doi.org/10.3390/rs11222645
• 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, 1745. https://doi.org/10.3390/rs10111745 -
Effects of agricultural management in the weed community
Guerra et al. (2021) A trait-based approach in a Mediterranean vineyard: Effects of agricultural management on the functional structure of plant communities. Agric. Ecosyst. Environ. 316, 107465. https://doi.org/10.1016/j.agee.2021.107465
Luna et al. (2020) Is pasture cropping a valid weed management tool? Plants 9, 135. https://doi:10.3390/plants9020135
