Francisca López-Granados

What is she best known for?

The fields of study she is best known for:

• Agronomy
• Artificial intelligence
• Statistics

Her primary scientific interests are in Remote sensing, Precision agriculture, Weed control, Multispectral image and Weed. Her Remote sensing research is multidisciplinary, incorporating perspectives in Mean squared error, Pixel, Early season and Vegetation. Her work on Image resolution expands to the thematically related Precision agriculture.

Her Weed control research incorporates themes from Weed detection and Environmental resource management. Her Multispectral image study combines topics from a wide range of disciplines, such as Image processing, Classifier, Segmentation and Normalized Difference Vegetation Index. Her Weed study necessitates a more in-depth grasp of Agronomy.

Her most cited work include:

• Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV (219 citations)
• Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. (213 citations)
• Configuration and specifications of an Unmanned Aerial Vehicle (UAV) for early site specific weed management. (189 citations)

What are the main themes of her work throughout her whole career to date?

Remote sensing, Precision agriculture, Weed, Agronomy and Weed control are her primary areas of study. Her biological study spans a wide range of topics, including Image resolution, Pixel and Vegetation. Her Precision agriculture study integrates concerns from other disciplines, such as Tree, Statistics and Support vector machine, Artificial intelligence.

Her work in Weed addresses subjects such as Spatial distribution, which are connected to disciplines such as Geostatistics. Her research in Weed control intersects with topics in Agroforestry and Image acquisition. Her Multispectral image research incorporates elements of Hyperspectral imaging and Satellite imagery.

She most often published in these fields:

• Remote sensing (36.63%)
• Precision agriculture (36.63%)
• Weed (33.66%)

What were the highlights of her more recent work (between 2017-2021)?

• Photogrammetry (8.91%)
• Remote sensing (36.63%)
• Precision agriculture (36.63%)

In recent papers she was focusing on the following fields of study:

Francisca López-Granados spends much of her time researching Photogrammetry, Remote sensing, Precision agriculture, Weed and Tree. As part of her studies on Remote sensing, Francisca López-Granados often connects relevant subjects like Vegetation. In her study, Vine and Contextual image classification is inextricably linked to Crop, which falls within the broad field of Precision agriculture.

Much of her study explores Weed relationship to Weed control. Francisca López-Granados interconnects Mean squared error, Statistics and Agricultural engineering in the investigation of issues within Tree. Her Agronomy research integrates issues from Artificial neural network and Weed detection.

Between 2017 and 2021, her most popular works were:

• An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery (92 citations)
• 3-D Characterization of Vineyards Using a Novel UAV Imagery-Based OBIA Procedure for Precision Viticulture Applications (48 citations)
• Assessing UAV-collected image overlap influence on computation time and digital surface model accuracy in olive orchards (46 citations)

In her most recent research, the most cited papers focused on:

• Agronomy
• Artificial intelligence
• Botany

Her main research concerns Remote sensing, Photogrammetry, Precision agriculture, Weed control and Weed. Spatial analysis and RGB color model is closely connected to Tree in her research, which is encompassed under the umbrella topic of Remote sensing. Her research integrates issues of Mean squared error and Similarity in her study of Photogrammetry.

Her Precision agriculture study frequently draws connections to adjacent fields such as Crop. Her Weed control study incorporates themes from Agroforestry, Segmentation and Arable land. Her work deals with themes such as Image resolution, Classifier, Algorithm, Random forest and Crop management, which intersect with Weed.