Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Data and Script for: Olive Trees Health and Yield Prediction through EO data and Machine Learning (OLEA-PRED) / EO AFRICA – Research and Development Facility

Domaine:

agriculturegeospatial
Créateur:
BouBouAicSal
Éditeur:
Zenodo
Hôte:avatar
This database was collected under the "Olive Trees Health and Yield Prediction through EO data and Machine Learning" project, funded by the European Space Agency in the framework of the "EO AFRICA R&D Facility". - Shapefiles.rar contains shp files for Orchads boundaries and tree locations. - Field_data contains data collected in the field (Chllorophyl, Yield and Soil ). - SCRIPTS.rar contains all scripts and extracted data used for prediction for Settat orchad. - FBS images.rar contains UAV and M6 images for Fkih Ben Saleh orchad.

Visit

doi.orgzenodo.org

Tasks

computer vision

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similaires

EO Africa - RFCMACC notebooks and dataEO AFRICA R&D Facility Research Projects 2022Improving Olive Yield Prediction Using Landsat Multispectral Data and Advanced Ensemble Learning in TunisiaEnhancing Crop Yield Prediction Using Machine Learning and Geospatial DataWALMOST EO AfricaMarcYin/EO-AFRICA

EO Africa - RFCMACC notebooks and data

Scripts and data created through the EO Africa project: Riverine flood and crop monitoring

EO AFRICA R&D Facility Research Projects 2022

In the framework of the ESA EO AFRICA initiative, the EO AFRICA R&D Facility in collaboration wi

Improving Olive Yield Prediction Using Landsat Multispectral Data and Advanced Ensemble Learning in Tunisia

ABSTRACT Olive cultivation is a key agricultural activity in

Enhancing Crop Yield Prediction Using Machine Learning and Geospatial Data

This study presents a geospatially informed machine learning approach to improve crop yield predicti

WALMOST EO Africa

The data used for acadja detection

MarcYin/EO-AFRICA

EO AFRICA # BIG data Archetypes for Crops from EO This work is part of the BIG data Archetypes f