Precision Agriculture through Sap Flow Perception in Plants
South Tyrol is one of Italy’s leading regions for apple production, yielding around one million tons annually—40% of the national and 10% of the European output. Alongside its renowned wine cultivation, fruit farming plays a central role in the region’s agricultural economy. The APPLE project aims to enhance orchard management by using advanced sensors to monitor sap flow in apple trees, enabling real-time health diagnostics.
The project focuses on three key goals:
- Real-time plant health monitoring: PlantVoice technology detects physiological stress and disease early, improving plant care and reducing environmental impact.
- Sensor and data system optimization: The project refines probes and electronics to suit orchard needs and develops an automated data analysis system that generates instant alerts.
- Accessible decision support: The system offers simple alerts for farmers and detailed data for agronomists, helping guide irrigation, fertilization, and pesticide strategies. Field validation is conducted with experts from the Laimburg research center through the LIDO collaboration.
The APPLE project – Precision Agriculture through Sap Flow Perception in Plants – addresses the needs of intensive apple cultivation in South Tyrol, a region responsible for 40% of Italy’s and 10% of Europe’s apple production. In such a context, efficient resource use and early detection of plant stress are essential for both economic and environmental sustainability. The project introduces the PlantVoice technology, which uses advanced sensors to monitor sap flow in real time, enabling early diagnosis of plant health conditions.
The initiative is structured in three phases: laboratory preparation, field testing, and data analysis. Sensors will be installed on sentinel apple trees in orchards subjected to controlled stress conditions (e.g., reduced irrigation, altered fertilization, absence of treatments). With the support of the Laimburg research center, data will be collected and analyzed using AI and traditional methods to generate alerts and identify stress-specific “fingerprints.”
The system is designed to be cost-effective and scalable, offering simple alerts for farmers and detailed insights for agronomists. It will serve as a decision-support tool for optimizing irrigation, fertilization, and pesticide use. The project will be validated in real-world conditions and disseminated through workshops, scientific publications, and agricultural media, aiming to enhance the competitiveness of local enterprises and promote environmental, social, and economic sustainability.
- Project duration: -
- Project status:
- Funding: Private organisations (Other projects /Project)
- Website: https://fusiongrant.info/it/fusion-grant/archivio/apple-agricoltura-di-precisione-mediante-percezione-della-linfa-nelle-piante
- Institute: Center for Sensing Solutions
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