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Facundo G.
@flgutierrez
Agronomist Master's Degree in Precision Agriculture
Argentinien
Englisch, Spanisch
Über mich
Agricultural Engineer | MSc Precision Agriculture | Computer Vision & Remote Sensing
I train YOLO object detection models and analyze satellite/drone imagery for agriculture, environmental monitoring, and land-use projects. My master's thesis was built around this exact workflow — from raw imagery to deployed model.
Core services: custom YOLO model training, satellite image analysis (NDVI, multispectral), dataset preparation & annotation, vegetation mapping, and crop analytics.
Tools: Python,QGIS,Ultralytics.
Message me with your project details and I'll let you know how I can help.
Kompetenzen
Facundo G.
offline •
Durchschnittliche Antwortzeit: 1 Stunde
Meine Dienstleistungen
Beratung
I will create vegetation maps from satellite or drone images
Maschinelles Lernen
I will a custom yolov8 object detection model with your images
Arbeitserfahrung
Self Employed
Selbstständig • 3 yrs 8 mos
Data Analyst — Power BI & Tableau Projects
May 2024 - Present • 2 yrs 2 mos
Designed interactive dashboards for business analytics, covering sales performance, customer segmentation, real estate pricing, and workforce survey analysis. Handled the full workflow: data cleaning, transformation, visualization, and insight delivery. Tools: Power BI, Tableau, SQL, DAX. Certified: Data Analysis and Visualization with Power BI — Microsoft (Coursera, 2024).
Remote Sensing Analyst
Sep 2025 - Jun 2026 • 9 mos
Conducted multispectral satellite image analysis to assess wheat crop health using NDVI indices. Generated vegetation maps and actionable reports for variable-rate fertilization decisions.
Computer Vision Engineer — MSc Research Project
Sep 2025 - Jun 2026 • 9 mos
Designed and deployed a YOLOv8 object detection system for real-time poultry monitoring in commercial broiler houses, aimed at early detection of heat stress through individual bird tracking and behavioral analysis. • Built a custom dataset of 6,000+ annotated images from overhead cameras installed in poultry houses, using CVAT for annotation. • Trained and fine-tuned YOLOv8 models for individual bird detection and behavior classification under varying environmental conditions. • Developed a monitoring pipeline to identify behavioral patterns associated with thermal stress, enabling early intervention and reducing mortality risk. • Tools: Python, Ultralytics YOLOv8, CVAT, OpenCV.
