HARVESTA – Cognitive AI Platform and Ecosystem for Intelligent Robotic Harvesting

Exhibitor

Area of exhibition:

Stand

Knowledge-generating body:

CIIC - Polytechnic University of Leiria

The HARVESTA project aims to design and develop innovative and accessible solutions based on core Industry 5.0 technologies (IoT, AI, Big Data, cyber-physical systems, autonomous robots) for application in the agricultural sector, as well as to promote sustainable, predictive, and autonomous agricultural practices that generate high added value to improve the agri-food industry.

The project enables the optimization of operational efficiency by integrating cognitive intelligence and autonomous robotics into the harvesting process and provides farmers with real-time data on crop conditions and growth patterns, allowing them to optimize their operations.

This ecosystem promotes more sustainable and autonomous agricultural practices, creating products with high added value. This technological initiative aims to strengthen the agri-food industry and boost the sector's national and international competitiveness, ensuring that technological innovation leads to economic prosperity and the preservation of natural resources.

Country:

Portugal

Region:

Sectors:

Circular Economy

SDG:

"SDG09: Industry, innovation and infrastructure"

Video Link:

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Applications

Automated harvesting: identification and harvesting of only those fruits that have reached the ideal ripeness index (color, size, and texture), minimizing human intervention.

Early harvest prediction: Capability to predict harvest timing in a proactive manner by analyzing the ripeness condition.

Autonomous operations: ability to harvest 24 hours a day, 7 days a week, solving labor shortages.

Real-time production estimates: accurate counting of fruit still on the plant for more accurate harvest forecasts, facilitating logistics management with distributors.

Early detection of pests and diseases: identification of leaf abnormalities or signs of water stress through multispectral vision before they are visible to the human eye.

Maturation mapping: creation of heat maps of the farm to identify which areas ripen faster, optimizing harvest planning.

Digital Twin: creation of a virtual environment that mirrors the greenhouse or orchard to simulate different growth scenarios and test the impact of climate change.

Resource optimization: cross-referencing IoT sensor data with AI to apply water and nutrients differently from plant to plant (precision agriculture).

Circular Economy

Exhibitor

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