Periodic Reporting for period 1 - SQAT (Soil Quality Analysis Tool: Implementing Smart Farming Applications using EO Data, Soil Sensors & Robotics)
Okres sprawozdawczy: 2024-02-01 do 2025-10-31
Agriculture is undergoing rapid digitalisation, yet farms continue to face increasing environmental and societal pressures related to soil health, including cleaner water, improved soil quality, better carbon storage, and enhanced biodiversity. A detailed understanding of soil variability is essential for effective decision-making, but current market solutions for soil mapping are often too costly, too complex, or insufficiently aligned with farmers’ needs.
The SQAT project aims to develop an integrated smart soil mapping service that combines multiple technologies to generate high-resolution soil property maps and a range of demand-driven application products, such as variable-rate application maps. The SQAT approach merges in-situ sampling and sensing, deployed on an autonomous robotic platform, with a Copernicus-based artificial intelligence soil mapping engine. This integrated system significantly reduces costs while increasing productivity compared to existing solutions.
The robotic sensing toolbox includes NIR sensors, an automated sampling drill, a penetrometer, and an innovative chamber for in-situ wet chemical analysis (“Lab in the Field”). Based on the produced maps, the project co-develops, tests, and validates five smart farming applications: variable-rate liming/fertilisation/seeding, variable-depth tillage, and carbon farming MRV.
Seven SMEs from across the soil data value chain participate in the project, each leading a use case in different European regions. With a strong market orientation from the outset, the project aims to ensure that results can be commercialised by the end of the project, while actively engaging farmers, agri-service providers, and other stakeholders in the development and adoption of SQAT-enabled smart farming applications.
The SQAT project aims to develop an integrated smart soil mapping service that combines multiple technologies to generate high-resolution soil property maps and a range of demand-driven application products, such as variable-rate application maps. The SQAT approach merges in-situ sampling and sensing, deployed on an autonomous robotic platform, with a Copernicus-based artificial intelligence soil mapping engine. This integrated system significantly reduces costs while increasing productivity compared to existing solutions.
The robotic sensing toolbox includes NIR sensors, an automated sampling drill, a penetrometer, and an innovative chamber for in-situ wet chemical analysis (“Lab in the Field”). Based on the produced maps, the project co-develops, tests, and validates five smart farming applications: variable-rate liming/fertilisation/seeding, variable-depth tillage, and carbon farming MRV.
Seven SMEs from across the soil data value chain participate in the project, each leading a use case in different European regions. With a strong market orientation from the outset, the project aims to ensure that results can be commercialised by the end of the project, while actively engaging farmers, agri-service providers, and other stakeholders in the development and adoption of SQAT-enabled smart farming applications.
During the reporting period, the SQAT consortium advanced the development and integration of the core components of the smart soil-mapping system. Significant progress was achieved in building and testing the autonomous robotic platforms for soil sampling and continuous sensing, including the heavy-duty sampler and the quadruped-mounted sampler. Prototypes of the proximal sensing module and the in-situ “Lab-on-the-Field” chemical analysis system were developed, including initial sensor synchronisation workflows. In parallel, implementation of Earth Observation workflows for soil zoning and sampling-location optimisation began across multiple use cases.
The project also established the full SQAT data pipeline, from data cleaning and fusion to the generation of soil property maps, supported by contributions from robotic sensing, EO data, penetrometer measurements, and in-situ laboratory outputs. Technical foundations were laid for the five Smart Farming Applications, including variable-rate fertilisation, seeding, and variable-depth tillage, with the first algorithmic workflows and early prototypes completed. Across the seven use cases in Europe, field sampling campaigns were initiated, providing initial datasets for calibration and validation and supporting the progress of system components and modelling approaches toward future integration and real-world deployment.
The project also established the full SQAT data pipeline, from data cleaning and fusion to the generation of soil property maps, supported by contributions from robotic sensing, EO data, penetrometer measurements, and in-situ laboratory outputs. Technical foundations were laid for the five Smart Farming Applications, including variable-rate fertilisation, seeding, and variable-depth tillage, with the first algorithmic workflows and early prototypes completed. Across the seven use cases in Europe, field sampling campaigns were initiated, providing initial datasets for calibration and validation and supporting the progress of system components and modelling approaches toward future integration and real-world deployment.
The SQAT project is developing a new generation of soil-mapping technologies designed to make high-resolution soil information accessible, affordable and practical for everyday farm management. Today, many farms still rely on uniform, field-level management practices, even though soil properties can vary substantially within a single field. This variability affects yields, resource efficiency, and environmental performance. Existing precision farming tools can address these challenges but are often complex, fragmented, and costly, making them difficult to adopt, especially for small and medium-sized farms.
SQAT goes beyond the current state of the art by combining several technologies that traditionally operate in isolation—robotic sampling, proximal sensing, in-situ chemical analysis, and satellite-based Earth Observation into one integrated system. By bringing these “pieces of the puzzle” together, the project is building a coherent, operational workflow for producing affordable, high-resolution soil maps and transforming them into practical recommendations for farm operations.
SQAT goes beyond the current state of the art by combining several technologies that traditionally operate in isolation—robotic sampling, proximal sensing, in-situ chemical analysis, and satellite-based Earth Observation into one integrated system. By bringing these “pieces of the puzzle” together, the project is building a coherent, operational workflow for producing affordable, high-resolution soil maps and transforming them into practical recommendations for farm operations.