Remote Sensing for Precision Agriculture and Weed Science

Departament of Crop Protection
Major research lines of “Remote Sensing for Precision Agriculture and Weed Science” Group are centered on the optimization of agrochemical applications by using site-specific strategies.

Main current research activities are focused on the following objectives:

  • To detect and map weeds and other agronomic variables in herbaceous and woody crops by using remote sensing techniques and imagery from high resolution satellite and unmanned aerial vehicles (UAV).
  • To design, develop and evaluate OBIA (Objects-Based-Image-Analysis) procedures for geospatial analysis, image feature extraction, segmentation, tree geometry and vegetation classification in crops using digital cameras and multispectral sensors.
  • To improve the workflows for image processing by developing Geostatistics algorithms and specific software “add-on”- “plug-in” for precision agriculture approaches as well as for an accurate georeferenciation / co-registration and classification of cropping systems.

Def_Since1999

TEAM

Francisca López-Granados

Research ScientistHead of group
flgranados@ias.csic.es

José Manuel Peña-Barragán

Ramón y Cajal Researcher

Currently at the Institute of Agricultural Sciences (ICA-CSIC)
jmpena@ica.csic.es
Ana Isabel de Castro Megías

Ana Isabel de Castro-Megías

Juan de la Cierva Researcheranadecastro@ias.csic.es

Jorge Torres-Sánchez

Postdoctoral Researcherjtorres@ias.csic.es
Francisco Manuel Jiménez Brenes

Francisco Manuel Jiménez-Brenes

Predoctoral Researcherfmjimenez@ias.csic.es

P. González-de-Santos, A. Ribeiro, C. Fernández-Quintanilla, F. López-Granados, M. Brandstoetter, S. Tomic, S. Pedrazzi, A. Peruzzi, G. Pajares, G. Kaplanis, M. Pérez-Ruiz, C. Valero, J. del Cerro, M. Vieri, G. Rabatel & B. Debilde. 2017. Fleets of robots for environmentally-safe pest control in agriculture. Precision Agriculture. doi:10.1007/s11119-016-9476-3.

F. López-Granados, J. Torres-Sánchez, A.I. de Castro, A. Serrano-Pérez, F.J. Mesas-Carrascosa & J.M. Peña. 2016. Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery. Agronomy for Sustainable Development, 36(4): paper 67. doi:10.1007/s13593-016-0405-7.

F.J. Mesas-Carrascosa, I. Clavero-Rumbao, J. Torres-Sánchez, A. García-Ferrer, J.M. Peña & F. López-Granados. 2016. Accurate ortho-mosaicked six-band multispectral UAV images as affected by mission planning for precision agriculture proposes. International Journal of Remote Sensing, 38(8-10): 2161-2176. http://dx.doi.org/10.1080/01431161.2016.1249311.

M. Pérez-Ortiz, J.M.Peña, P.A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez & F. López-Granados. 2016. Selecting patterns and features for between- and within- crop-row weed mapping using UAV imagery. Expert Systems with Applications, 47: 85-94. doi:10.1016/j.eswa.2015.10.043.

F. López-Granados, J. Torres-Sánchez, A. Serrano-Pérez, A.I. de Castro, F.J. Mesas-Carrascosa & J.M. Peña. 2016. Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds. Precision Agriculture, 17(2): 183-199. doi:10.1007/s11119-015-9415-8.

J. Torres-Sánchez, F. López-Granados, N. Serrano, O. Arquero & J.M. Peña. 2015. High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology. PLOS ONE, 10(6): e0130479. https://doi.org/10.1371/journal.pone.0130479.

J. Torres-Sánchez, F. López-Granados & J. M. Peña. 2015. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture, 114: 43–52. doi:10.1016/j.compag.2015.03.019.

J. M. Peña-Barragán, J. Torres-Sánchez, A. Serrano-Pérez, A. I. De-Castro & F. López-Granados. 2015. Quantifying efficacy and limits of Unmanned Aerial Vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors, 15: 5609-5626. doi:10.3390/s150305609.

L. García-Torres, J. J. Caballero-Novella, D. Gómez-Candón & J. M. Peña-Barragán. 2015. Census Parcels Cropping System Classification from Multitemporal Remote Imagery: A Proposed Universal Methodology. PLOS ONE, 10(2): e0117551. doi:10.1371/journal.pone.0117551.

J. Torres-Sánchez, J.M. Peña, A.I. de Castro & F. López-Granados. 2014. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers & Electronics in Agriculture, 103: 104-113. doi:10.1016/j.compag.2014.02.009.

L. García-Torres, J.J. Caballero-Novella, D. Gómez-Candón & A.I. De-Castro. 2014. Semi-Automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features. PLOS ONE, 9(3): e91275. http://dx.doi.org/10.1371/journal.pone.0091275

  • Image acquisition using unmanned aerial systems (UAS, UAV, drones) and creation of orthomosaics
  • Image analysis for agriculture and forestry applications:
    • Mapping weeds at different phenological stages
    • Phenotyping (genetic varieties) according to phenological stages
    • Mapping yield and crop status
    • Tree geometry and volume of woody crops and forest

Dr. Francisca López-Granados, Research Scientist. Head of group. flgranados@ias.csic.es

 Dr. Montserrat Jurado-Expósito, Tenured Scientist. montse.jurado@ias.csic.es

 Dr. José Manuel Peña-Barragán, Ramón y Cajal Researcher. jmpena@ica.csic.es

Dr. Ana Isabel de Castro-Megías, Juan de la Cierva Researcher. anadecastro@ias.csic.es

Dr. Jorge Torres-Sánchez, Postdoctoral Researcher. jtorres@ias.csic.es

Francisco Manuel Jiménez-Brenes, Predoctoral Researcher. fmjimenez@ias.csic.es