Studentische Arbeiten und Projekte

Die an unserem Lehrstuhl durchgeführten Arbeiten befassen sich mit dem Software- und Systems Engineering im Bereich Digital Farming. Hierfür betrachten wir Systeme entlang der gesamten Wertschöpfungskette. Unser Standort in Rheinland-Pfalz bietet durch die vielseitige Abdeckung verschiedener Bereiche, wie Wein-, Gemüse- und Getreideanbau, aber auch der unmittelbaren Nähe zu Forschung und Industrie eine hervorragende Grundlage.

Interessierte Studierende auf der Suche nach Abschlussarbeiten und Projekten sind stets willkommen, mit uns Kontakt aufzunehmen, um Interessen und mögliche Themen zu besprechen.

Unsere Forschungsschwerpunkte:

  • Software- und Systems Engineering im Bereich Digital Farming
  • Anforderungsanalyse der verschiedenen Akteure im landwirtschaftlichen Ökosystem
  • Verbesserung der Interoperabilität und Vernetzung zwischen Akteuren und Systemen
  • Verbesserung der Nutzungsakzeptanz

Offene Abschlussarbeiten


Crop Disease Recognition with Limited Data

Research Objectives:

  • Investigate the current state-of-the-art methods in crop disease recognition and their limitations, particularly concerning data scarcity.

  • Propose novel strategies for effectively utilizing limited data in training deep learning models for crop disease recognition.

  • Design and implement a deep learning framework tailored for crop disease recognition, emphasizing techniques such as transfer learning, data augmentation, and semi-supervised learning.

  • Evaluate the proposed framework on diverse crop disease datasets with varying degrees of data scarcity, comparing its performance against baseline methods.

  • Analyze the effectiveness of different techniques employed in the proposed framework and provide insights into their contributions to model generalization and robustness.

  • Explore potential applications and implications of the developed framework in real-world agricultural settings, considering factors such as scalability, computational efficiency, and practical usability.

Expected Contributions:

  • Development of a novel deep learning framework for crop disease recognition with limited data, incorporating innovative techniques to address data scarcity challenges.

  • Empirical evaluation of the proposed framework on diverse crop disease datasets, demonstrating its effectiveness and robustness compared to existing methods.

  • Insights into the effectiveness of different strategies employed in handling limited data for crop disease recognition, providing guidance for future research and practical applications in agriculture.

Contact:

Vishal Sharbidar Mukunda

 


Exploring the impact of explainability on the user acceptance of Digital Farming solutions: Bachelor Thesis

Overview:

Digital Farming solutions, such as precision agriculture technologies, crop management software, soil irrigation systems, have the potential to increase agricultural productivity, reduce costs, and improve sustainability. However, the adoption of these technologies by farmers has been slow. One reason for this could be missing explainability how these systems come to decisions. The opaque nature of these models limits the farmer's understanding of the technology and their ability to trust the system.

The goal of this study is:

  • To identify the key factors that influence farmers' acceptance of Digital Farming solutions.
  • To explore the role explainability plays in user acceptance.
  • Investigate how explainability is integrated in other domains (e.g. medical, finance).
  • Provide recommendations for the development of explainable Digital Farming solutions in agriculture.

Methodologies:

  • Online literature review
  • Surveys and interviews to measure the relationship between explainability, user understanding, trust, and acceptance

Contact :

Mengisti Berihu