Water management and sensing of olives, vines and citrus are prominent. Spraying requires separate checks of regional procedures, products and operators.
IAS-CSIC records UAV crop-stress work in the 2000s, followed by applications such as APOGEO island viticulture and MEDHUSA soil-moisture validation.
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Treats drone pesticide application as aerial spraying and separates product, operator and regional treatment permissions.
Describes agricultural and forestry research sensing using RTK, RGB, multispectral, thermal and LiDAR equipment.
A research lecture connects drone and satellite sensing with models of plant water status, physiology and soils.
A Macaronesian vineyard project combines multispectral observation and sample analysis; early detection is a project objective.
MEDHUSA validates a UAV-mounted passive reflectometer for soil moisture using reflected satellite signals and field measurements.
Research history spans 2000s UAV crop-stress work and current image automation, highlighting calibration and geometric processing.
Advises defining the farm problem before choosing ownership or contracting and stresses interpretation and legal limits.
Connects drone and satellite vigour maps to variable application by ground sprayers in commercial vineyards and olive groves, with efficacy validation and adoption training.
Combines drone canopy cover with the open-source AquaCrop-OSPy crop model for broccoli yield estimation; further-season validation remains important.
Course announcement covering application physics, experimental evaluation, ISO standards and regulatory challenges; attendance is not an application authorisation.
Research communication on mapping weed location and species from UAV imagery to support targeted treatment rather than uniform field application.
Development project combining UAVs, IoT and AI for pests and diseases in woody crops. Swarm and cloud concepts are research objectives, not evidence of a finished diagnostic service.
Early maize weed-mapping case using visible and near-infrared UAV imagery, algorithms and ground observations, illustrating the development of targeted application workflows.
Research programme joining remote sensing, GIS and robotics, including UAV LiDAR canopy structure and active/passive sensing in irrigated and rain-fed olive systems.
Pilot in northeastern Spanish maize fields links UAV weed maps to ground-sprayer prescriptions. Misses within crop rows and at low weed density qualify the reported savings.
Alaire explores regional airspace, 5G corridors and U-Space infrastructure with agriculture among its applications. Planned infrastructure should not be presented as operational deployment.
Field teaching connects mission planning and multispectral acquisition over barley with processing in tools including QGIS; usable agronomic information requires more than piloting.
Project seeks hyperspectral and thermal detection, localisation and severity assessment of olive diseases. Its development objectives are distinct from independently demonstrated diagnostic accuracy.
Reviews drone roles in early targeted intervention, later weed escapes and resistance, weed-suppressive crop breeding and landscape biodiversity within integrated weed management.
Uses aerial and ground vegetation sensing to prescribe spray volume and airflow, with planned agronomic, economic and environmental evaluation.
Research history traces regional satellite weed mapping and later UAV early-detection projects, showing the evolution of sensing and image-analysis methods.
Describes research links with Brazil and a woody-crop trial in Cuenca, using multispectral mapping for differentiated treatment; trial outcomes are not general regulatory approval.
Research abstract assesses spatial and spectral quality and weed-detection accuracy after resampling UAV imagery, informing resolution and data-volume tradeoffs.
Olive project pairs airborne sensors with laboratory samples to investigate nutrition, ripeness, soil moisture and pests. The page includes intended outcomes rather than demonstrated performance alone.
Extremadura aerial-application procedure distinguishes drone operator registration, equipment inspection, pesticide-handling competence and insurance requirements.
The autumn 2026 public training programme covers drones, remote sensing, application and robotics, illustrating the role of demonstrations and skills in adoption.
Institutional description of a CSIC spinout translating individual-tree analysis into irrigation decisions. It establishes the platform use case, not independent proof of savings or yield gains.
Almond and olive studies show why thermal recovery need not mean photosynthetic recovery, motivating combined fluorescence, spectral and temperature measurements.
Two-season study in a commercial Villena olive orchard validates thermal and multispectral indicators against plant measurements while identifying environmental and sensor limitations.
Indexed text describes citrus demonstrations, aviation and application rules, vegetation indices and water stress training. The full page was access-restricted and is not marked as read.
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