Cofinanced projects

SISCOG - Why choose SISCOG
Project designation

AI4OptiAgri - AI and Data Science Solutions for Optimizing Precision Agriculture

Operation code: COMPETE2030-FEDER-00630300, LISBOA2030-FEDER-00630300 (projeto n.º 14616)

Main goal: To optimize agricultural production sustainably and ensure the efficient and balanced use of resources, delivering clear benefits for both agriculture and the environment.

Region of intervention: Lisboa, Centro, and Alentejo

Beneficiary entity: SISCOG - Sistemas Cognitivos, SA and Instituto Nacional de Investigação Agrária e Veterinária, I.P.

 

Approval date:     26-09-2024

Starting date:       01-03-2024

Completion date: 28-02-2027

Total eligible cost: 2.299.242,08 Euros

European Union financial support: 1.130.146,71 Euros

 

Goals, activities and expected results:

The AI4OptiAgri project is a collaborative initiative  by a consortium (SISCOG and INIAV) to optimize agricultural production sustainably and promote the efficient and balanced use of resources, delivering clear benefits for both agriculture and the environment.  

The project addresses 7 key areas: phenology, crop coefficients, water stress intensity, nutritional status, disease detection, phenotyping platform, and yield estimation. These will be tested and validated across 4 agricultural sectors – vineyards, olive, fruit, and almond plantations – to develop and assess the prototype.
  
It aims to combine 4 innovative technologies – remote sensing with multiple types of sensors, data fusion, AI, and Data Science – to create sophisticated and powerful models. These models will extract valuable knowledge from complex and rich data to support decision-making in agriculture production.

 

Operation Sheet

 

Project designation

RAPID - Resource Allocation and Planning IntegrateD

Operation code: LISBOA2030-FEDER-01057000 (projeto n.º 17222)

Main goal: The RAPID project focuses on introducing innovative techniques and components into SISCOG's products, aiming to keep the company ahead of the competition in an international market dominated by large corporations operating in its niche sector.

Region of intervention: Lisboa

Beneficiary entity: SISCOG - Sistemas Cognitivos, SA

 

Approval date:     13-09-2024

Starting date:       16-04-2024

Completion date: 14-04-2026

Total eligible cost: 760.759,29 Euros

European Union financial support: 304.303,72 Euros

 

Goals, activities and expected results:

The RAPID project focuses on introducing innovative techniques and components into SISCOG’s products.  

Founded in 1986 as an Artificial Intelligence company, SISCOG competes in the international market alongside large corporations operating in its niche sector. The company’s success, and its survival, hinges on the innovation it brings to its products, keeping it ahead of the competition.  

The developments proposed in this project are aligned with SISCOG’s 2024–26 strategic plan. The requested funding  represents an opportunity for the Portuguese Government to support a company that has been active in international markets since 1993, with around 90% of its revenue stemming from exports to Northern European and American countries. It also provides a means to foster advancements in the strategic field of Artificial Intelligence.

 

Operation Sheet

SISCOG - Why choose SISCOG
Project designation

SPARK - Siscog Prospecting new mARKets

Project code: LISBOA-02-0752-FEDER-071247

Main goal: Reinforce competitiveness of small and medium enterprises

Region of intervention: Lisboa

Beneficiary entity: SISCOG - Sistemas Cognitivos, SA

 

Approval date:     28-04-2021

Starting date:       26-04-2021

Completion date: 30-06-2023

Total eligible cost: 539.053,14 Euros

European Union financial support: 215.621,26 Euros, through the European Regional Development Fund

 

Goals, activities and expected results: SISCOG has been dedicated to the development of systems for the optimized planning and management of resources for the passenger transport sector at an international level. Currently, in addition to Portugal, it has clients in 7 countries, in 2 continents. Almost 100% of the turnover comes from software exports totally "made in Portugal".

The objectives of this project are:

  • strengthen international turnover,
  • by attracting new clients and
  • diversifying current markets, and
  • launching a new area related to Data Science and Machine Learning.

 

Project Sheet

 

Project designation 

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, Alentejo

Beneficiary entity: 

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, 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