Objectives of the team
- 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
Main recent achievements
- Revisión de las tecnologías de vanguardia utilizadas en monitorización de malas hierbas.
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
- Fenotipado y caracterización 3D de cultivos con drones y su aplicación a diversos objetivos agronómicos.
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
- Procedimientos de inteligencia artificial para explotar el uso de la teledetección en agricultura.
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
- Nuevas metodologías para la construcción de modelos tridimensionales de malas hierbas y cultivo usando cámaras de profundidad y fotogrametría.
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. https://www.mdpi.com/1424-8220/18/4/1077
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. https://www.mdpi.com/1424-8220/16/7/972
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. https://doi.org/10.1016/j.compag.2016.01.018
