Ms. Mar Ariza-Sentís | Precision Agriculture Award | Young Scientist Award
Ms. Mar Ariza-Sentís, Wageningen Univresity & Research, Netherlands
Publication Profile
Professional Background
Maria del Mar Ariza-Sentís is a Ph.D. candidate at Wageningen University, specializing in Geo-Information Science. 🎓 With a master’s degree from Wageningen and a bachelor’s from the University of Lleida, she’s earned recognition for her academic achievements. Maria’s expertise extends to AI and UAV technology, evident through her role as an AI expert for the FlexiGroBots project, aiming to reduce pesticide use in agriculture. 🌿 Her diverse professional experience includes internships, teaching positions, and contributions to academic research, highlighting her commitment to advancing agricultural innovation.
Education and Awards
Maria’s dedication to continuous learning is evident through her complementary education, including a EU Drone license and qualifications in phytosanitary product handling. 🛩️ Her academic excellence has been recognized through numerous honors and awards, reflecting her commitment to excellence in research and academia.
Research Focus
Publication Top Notes
🌿 Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service
📄 Cited by: 221
📅 Year: 2016
🍇 Mapping the spatial variability of Botrytis bunch rot risk in vineyards using UAV multispectral imagery
📄 Cited by: 40
📅 Year: 2023
🔍 Object detection and tracking on UAV RGB videos for early extraction of grape phenotypic traits
📄 Cited by: 17
📅 Year: 2023
📸 Dataset on unmanned aerial vehicle multispectral images acquired over a vineyard affected by Botrytis cinerea in northern Spain
📄 Cited by: 11
📅 Year: 2023
🌱 Estimation of spinach (Spinacia oleracea) seed yield with 2D UAV data and deep learning
📄 Cited by: 11
📅 Year: 2023
🌾 Mapping of Rumex obtusifolius in nature conservation areas using very high resolution UAV imagery and deep learning
📄 Cited by: 11
📅 Year: 2022
🍇 Dataset on UAV RGB videos acquired over a vineyard including bunch labels for object detection and tracking
📄 Cited by: 10
📅 Year: 2023
🔄 BBR: An open-source standard workflow based on biophysical crop parameters for automatic Botrytis cinerea assessment in vineyards
📄 Cited by: 3
📅 Year: 2023
🌡️ Comparing Nadir and Oblique Thermal Imagery in UAV-Based 3D Crop Water Stress Index Applications for Precision Viticulture with LiDAR Validation
📄 Cited by: 2
📅 Year: 2023
🌽 Vegetation indices from unmanned aerial vehicles–mounted sensors to monitor the development of maize (Zea mays L.) under different N rates
📄 Cited by: 2
📅 Year: 2015
🔍 Object detection and tracking in Precision Farming: a systematic review
📄 Cited by: 1
📅 Year: 2024