Development of an Ontology-Based Visual Analytics Framework for Enhanced Agricultural Field Trial Interpretation and Decision-Making

Short description: This thesis aims to develop an ontology-driven visual analytics framework tailored to agricultural field trials, focusing on transforming complex, multidisciplinary data into actionable insights. The research will integrate knowledge modelling and advanced visual analytics to interpret data from field experiments, including crop treatments, climate variables, and phenomics.

Keywords:  Cartography, Geospatial Analytics, Ontology Design, Agricultural Field Trials, Visual Analytics

Topic at: TU Munich

Staff involved: Dr.-Ing. Ekatarina Chuprikova (Eurac Research) (Ekaterina.Chuprikova@eurac.edu)  

Description:

This thesis focuses on the intersection of cartography, geospatial data, and semantic data integration to develop visual analytics tools that empower domain experts—such as agronomists and orchard managers—to analyze and visualize field trial data effectively, both in the field and the lab. By leveraging geospatial data and cartographic principles, the research highlights the spatial variability inherent in agricultural experiments, particularly those employing the Randomized Complete Block Design (RCBD). RCBD is an experimental framework where treatments are randomized within blocks to reduce variability and enhance comparative accuracy.

The study emphasizes the development of an ontology tailored to experimental treatments and field-specific factors, integrating data on treatments, climate variables, and crop genomics and phenomics. Cartographic methods play a pivotal role in visualizing this information, enabling users to map treatment responses, environmental stress patterns, and spatial correlations critical for understanding agricultural practices.

Through a comprehensive process—including geospatial data processing, cartographically informed visualization design, and semantic data integration—the thesis aims to create a visual analytics platform that bridges the gap between complex agricultural datasets and actionable insights. The result is a tool that not only improves decision-making for field trials but also advances geospatial representation techniques in agricultural research. This work underscores the importance of cartography in managing and visualizing the spatial dimension of field trial data, paving the way for more sustainable and precise agricultural practices.

The topic is offered by the Center of Sensing Solutions, EURAC Research located in Bolzano, Italy. The Center specializes in tailor-made solutions for research and industry, from sensor-based data collection to advanced data analysis and the development of web applications or services. Students pursuing this thesis will benefit from the multidisciplinary expertise of a dynamic team combining data science, technology, computer engineering, and electronics to develop innovative tools for sustainable solutions. Bolzano is a vibrant Alpine city blending Italian and German cultures. Situated in the modern NOI Techpark, EURAC Research provides a dynamic and collaborative environment equipped with state-of-the-art facilities, including a sensor lab and digital platforms. The Center actively supports open-source technologies, fostering trustworthy and reliable data exchange, and creating cross-domain data value chains. A scholarship may be available for students willing to relocate to Bolzano.

Figure 1 (above): Visualizing the Interplay of Data and Space (credit: Agmatix)

Figure 2 (below): Project workflow

Literature/references:

  1. Chergui, N. and Kechadi, M.T., 2022. Data analytics for crop management: a big data view. Journal of Big Data, 9(1), p.123.
  2. Chen, Y., Wu, C., Zhang, Q. and Wu, D., 2023. Review of visual analytics methods for food safety risks. Npj Science of Food, 7(1), p.49.
  3. Casler, M.D., 2018. Blocking principles for biological experiments. Applied statistics in agricultural, biological, and environmental sciences, pp.53-72.