Objectives of the team

  1. 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
  1. 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
  1. 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