Within the context of the digital transformation of European agriculture, the challenge is to develop intelligent systems capable of precisely, sustainably, and cost-effectively monitoring crop health. The goal is to optimize yields and reduce pesticide use, in line with the European Farm to Fork strategy and broader agricultural sustainability objectives. The HortiQD project, funded under the Horizon Europe program, was created to address this need using advanced computer vision technologies and agricultural automation.
The European consortium, led by Fraunhofer ENAS and composed of companies and research centers such as ST Microelectronics, SITIA, CEA, ABS, InHort, and RESI S.p.A., collaborated to create an innovative monitoring system for orchards and intensive vegetable farms.
RESI S.p.A., an Italian leader in ICT solutions and Service Assurance, was responsible for designing and implementing the DCCS (Data Collection & Correlation System) and leads Work Package 3 – “System optimization, integration and algorithm teaching.”
The DCCS collects and correlates data from multiple sources — field sensors, maps, historical and forecasted weather data, and environmental time series — to provide an integrated view of orchard health. A new near-infrared hyperspectral sensor, mounted on an autonomous tractor, enables real-time crop monitoring (particularly apple orchards). The processed data is then delivered as maps and mission reports, providing decision support for farmers.
The HortiQD project represents a tangible step toward smarter, more efficient, and environmentally sustainable agriculture. The integration of advanced sensors, artificial intelligence, and automation has delivered significant results in both technological innovation and operational impact.
- Increased crop productivity and quality through continuous monitoring and objective data
- Reduced use of chemical inputs in line with European sustainability targets
- Improved operational efficiency through predictive analytics and automated alerts
- Advancement of agricultural automation with integrated data collection and correlation systems
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