AI4RealAG

A need felt around the world is to increase agricultural production sustainably and use resources efficiently and in a balanced way.

AI4RealAg – Artificial Intelligence and Data Science solutions for the implementation and democratization of digital agriculture

Project code: LISBOA-01-0247-FEDER-069670, POCI-01-0247-FEDER-069670

Main goal: Reinforce research, technological development and innovation

Region of intervention: Lisboa, Center and Alentejo

Beneficiary entities:

SISCOG – Sistemas Cognitivos, S.A.
Beyond Vision – Sistemas Móveis Autónomos de Realidade Aumentada, Lda
Instituto Nacional de Investigação Agrária e Veterinária, I.P.

Approval date: 24-05-2021

Starting date: 01-09-2020

Completion date: 30-06-2023

Total eligible cost: 2.661.843,68 Euros

European Union financial support: 1.562.945,17 Euros, through the European Regional Development Fund

Goals, activities and expected results:
AI4RealAg is a research project, developed by the consortium composed of SISCOG, INIAV and BEYOND VISION, that aims to increase agricultural production and quality, ensuring a positive impact on agricultural and environmental sustainability.

The project aims to:

  • Develop Artificial Intelligence (AI) and Data Science models that, through the analysis of large volumes of data, enable to uncover hidden knowledge from data, such as patterns, trends and correlations, which support smarter decision-making, as well as preparation of forecasts;
  • Develop a combined remote multispectral, thermal, 4K, 360º and LiDAR sensing, through the exploration of increasingly larger drone payloads, in order to increase the quality of the data that feed AI and Data Science models, consequently, improving the quality of results produced by them.

The project addresses six topics:

  • Characterization of the phenological states of cultures;
  • Determination of cultural coefficients;
  • Estimation of the intensity of water stress;
  • Diagnosis of nutritional status;
  • Health diagnosis for early detection of diseases; and
  • Development of an advanced phenotyping platform.

It will be tested and validated in three agricultural sectors:

  • Vineyard
  • Olive groves
  • Fruit trees orchards

Project Sheet