Student work and projects
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)
Open Theses
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.
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