The work carried out at our chair is concerned with software and systems engineering in the field of digital farming. For this purpose, we consider systems along the entire value chain. Our location in Rhineland-Palatinate offers an excellent basis due to the versatile coverage of different areas, such as wine, vegetable and grain cultivation, but also the immediate proximity to research and industry.

Interested students looking for theses and projects are always welcome to contact us to discuss interests and possible topics.

Our research focus:

• Software and systems engineering in the field of digital farming
• Requirements engineering of the different actors in the agricultural ecosystem
• Improvement of interoperability and networking between actors and systems
• Improvement of the acceptance of digital farming solutions (e.g. FMIS, decision support systems, agricultural machinery)

Motivation:

The accurate quantification of greenhouse gas (GHG) emissions is a critical component of sustainable agriculture, enabling farmers and policymakers to track and mitigate the environmental impacts of farming practices. Various farm-level GHG calculators have been developed to estimate carbon footprints, each utilizing different methodologies, emission factors, and system boundaries. Despite their widespread use, these tools often produce divergent results when applied to the same farm data, raising concerns about their reliability and comparability. The discrepancies in carbon footprint trends across different calculators may arise from variations in calculation assumptions, data inputs, and model parameters. This study aims to assess the reliability of multiple farm-level GHG calculators by comparing their carbon footprint estimations over multiple years for identical farm data. By identifying the methodological factors contributing to these variations, the research will provide insights into the consistency of these tools and their implications for farm-level decision-making. The findings will contribute to the ongoing efforts to harmonize GHG calculation methodologies and improve the accuracy of emissions accounting in agriculture.

Research Objectives:

• Systematic Literature Review
  • Study existing farm-level GHG calculation methodologies.
  • Identify key parameters influencing carbon footprint estimations.
• Data Collection & Tool Selection
  • Select set of GHG calculators for the study.
  • Gather real farm data or create a structured dummy dataset.
  • Define system boundaries and key input variables.
• Comparative Analysis
  • Input identical datasets into different tools and record results.
  • Analyse discrepancies in carbon footprint trends over multiple years.
• Interpretation & Impact Assessment
  • Evaluate how discrepancies in results affect farm management decisions.
  • Identify sources of variation (e.g., emission factors, soil carbon modelling).
• Conclusions & Recommendations
  • Discuss the limitations of current tools and provide recommendations to improve consistency and transparency in emissions accounting.

Requirement:

• Computer science studies
• Enthusiasm for agricultural issues
• Proficiency in German is a bonus, but not mandatory

Contact:

M.Sc. Shinu Philippose Jose

Introduction: 

The agricultural sector is increasingly integrating artificial intelligence (AI) technologies to increase efficiency, optimize use of resources to improve decision-making, and enhance productivity. Despite these advancements, the complexity and opacity of many AI systems pose a challenge for trust and acceptance among farmers. Explainable artificial intelligence (XAI) techniques aim to address these issues by providing transparency into AI decisions, making them understandable to users. While XAI applications are well-researched in other fields, their role in addressing agricultural challenges is still under-explored. Objective: This study aims to investigate how XAI techniques are applied to agricultural challenges through a systematic literature review (SLR). Objectives include identifying important agricultural problems that use XAI, categorizing the techniques used, and evaluate their effectiveness. The ultimate goal is to provide actionable insights for researchers and practitioners to improve the use and adoption of XAI in agriculture.

Research questions: 

RQ1: What are problems in dairy farming where XAI has been applied? 

RQ2: Which XAI techniques are most commonly used in dairy farming? 

RQ3: How effective are these XAI techniques at addressing specific agricultural challenges?

Contact: 

mengisti.berihu(at)rptu.de