AGILEHAND TEAMS UP WITH MURE.AI TO ENABLE SMARTER, FASTER AND GENTLER BERRY HARVESTING
Use case in a Nutshell
Sweetberry, a small producer of high-quality blackberries, is aiming to scale its production and introduce automation into sorting and packaging. However, manual handling remains slow, labour-intensive, and risks damaging the delicate fruit.
Through AGILEHAND, Mure.AI and three research partners — Fraunhofer, Tampere University and Universitat Politècnica de València — are collaborating to optimise harvesting timing and support gentler, more accurate sorting.
A key component of this collaboration is Mure.AI’s new weather-prediction station, which provides insights into the ideal harvesting window to ensure fruit quality and operational efficiency.
About the technology
SMART SENSING PACKAGE
The collaboration is built around the Smart Sensing Package, a modular multimodal sensor-and-AI platform developed within the AGILEHAND ecosystem for real-time environmental detection and classification.This Smart Sensing Package enables robust, continuous operation under variable field conditions, integrating low-power embedded hardware, sensor fusion, and AI models tailored for 24/7 agricultural deployment.
About the collaboration partners
Brings strong experience in robotics, smart sensing, and adaptive systems for handling delicate agricultural products.
Provides research knowledge in AI, machine learning, and intelligent systems for industrial and agricultural contexts.
A leader in applied research, contributing expertise in automation, sensing and system integration.
Mure.AI develops AI-enabled sensing and prediction tools for agriculture, combining weather analytics, machine learning and edge technologies to support efficient and high-quality crop processing.
Description of the Collaboration
Problem Addressed
Sweetberry relies on manual sorting and packaging, which is:
slow and labour-intensive
highly prone to fruit damage
unable to meet increasing demand
difficult to scale sustainably
Ensuring that berries are harvested at the right moment, and then sorted gently and accurately, is essential for maintaining premium quality while growing production
Solution
The collaboration integrates AGILEHAND technologies with Mure.AI’s new weather-prediction station, creating a system that:
identifies optimal harvesting conditions using real-time micro-climate data
supports staff with better timing and decision-making
enhances sorting and handling workflows through AI-assisted sensing
This enables Sweetberry to reduce fruit damage, improve consistency, and increase handling efficiency, while preparing for future automation steps.
Execution
The AGILEHAND partners are jointly carrying out three key activities:
Weather-based harvesting optimisation
Mure.AI’s station collects environmental data and predicts the ideal harvesting window to maximise berry quality and reduce handling losses.Support for sorting and packaging workflows
AGILEHAND partners apply smart sensing and adaptive-handling expertise to complement manual sorting and prepare the ground for future automation.Knowledge transfer and system validation
Fraunhofer, Tampere University and UPV provide research, testing and integration support to help refine the system with real producer feedback.
Together, these steps aim to improve efficiency without compromising fruit delicacy, enabling Sweetberry to scale sustainably.
Host Role (Fraunhofer, Tampere University and Polytechnic University of Valencia)
The AGILEHAND partners — Fraunhofer, Tampere University, and Universitat Politècnica de València — act as the technology hosts, providing research expertise, testing environments and technical guidance.
They support the design, validation and integration of Mure.AI’s weather-based sensing approach, ensuring the solution aligns with AGILEHAND’s smart sensing, adaptive handling and reconfigurable system packages.
Beneficiary Role (Mure.AI)
Mure.AI is the industrial beneficiary, applying the hosted research and technological insights to develop and refine its new weather-prediction station.
By integrating the AGILEHAND partners’ knowledge into its system, Mure.AI can better support producers like Sweetberry with optimised harvesting timing, improved sorting workflows and higher fruit quality during scaling.
Results of the Collaboration
This use case is currently in progress, and therefore no final results are available yet.
Project info:
This use case is part of Fraunhofer, Tampere University and Polytechnic University of Valencia involvement in the European Union’s Horizon Europe research and innovation programme AGILEHAND, under Grant Agreement no. 101092043.
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