Description
This research group studies the factors that influence the development of the main pest organisms, using advanced monitoring and geospatial data analysis technologies, such as near and remote sensors, GIS and specialized software. The integration of these tools with Decision Support Systems allows for the optimization of intelligent pest control. They also develop customized solutions, mostly open source, for the analysis of images and remote sensing data, generating key information on agricultural and agroforestry systems, such as the 3D characterization of canopies, physiological and phytosanitary status, and the estimation of water stress and water consumption.
Scientific objetives
Use of geospatial technologies, artificial intelligence and physical models for the study of agroforestry systems at different scales, with application in:
- Precision agriculture and localized management of pest organisms.
- Monitoring of biodiversity and invasive species.
- Soil-plant-atmosphere interactions in the critical zone of the Earth.
Recent Highlights
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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