Funded Projects

We are conducting research in several funded projects. You can find more information about them below. If you are interested in more details and discussions about our projects, do not hesitate to contact us.
For more information please contact Dominik Grimm.

PlantGrid (2020-2023)

funded by Federal Office of Food and Agriculture

The demand for horticultural products is depending on various external factors making it hard to predict sales. In practical operation, this may lead to out-of-stock situations possibly causing missed sales as well as excess-stock cases, which can result in the destruction of products. Besides financial loss, the latter is also generating environmental damage due to wasted resources during production and transport. These matters encourage conducting research in this area. Hence, the research project Digital management support systems for small and medium-sized enterprises in value chains of ornamental plants, perennials and cut flowers (acronym: PlantGrid) was initiated. The goal of this project is to support small and medium-sized horticultural companies with a digital management system. This system is supposed to improve planning and disposition of goods, e.g. in order to reduce out-of-stock and excess-stock situations. Moreover, we will take businesses along the whole horticultural value chain starting with producers leading to wholesalers and ending with retailers into account.

Besides several horticultural companies and the digital agency snoopmedia, PlantGrid is done in cooperation with the professorships Marketing and Management of Biogenic Resources and Retail Management of the University of Applied Sciences Weihenstephan-Triesdorf as well as the professorships Logistics Management and Horticultural Economics of the University of Applied Sciences Geisenheim.

One essential part of this project are Multivariate Time Series Predictions using historical observations and external factors. This part is the main focus of the bioinformatics lab within the project. At the beginning, we will examine internal and external data sources potentially relevant for predictions in horticulture. First, we will explore the retail sector, and evaluate models using preliminary datasets of a selection of companies as well as external data sources. Initially, we will apply methods like Exponential Smoothing and ARIMA or its extensions, which serve as baselines and comparison partners. Afterwards, modern ML-based methods will be evaluated, especially in a multivariate context. Finally, we will examine the combination of different predictors using simple and performance-based techniques. The steps described above should lead to a robust and precise prediction model for the horticultural retail sector. Next, we will enhance the results with additional information along the value chain. Furthermore, handling data heterogeneity and insufficiency are interesting research directions for the proposed project and therefore a central point for the bioinformatics lab.

You can find more information in German here.

EWIS (2020-2023)

funded by Bavarian State Ministry for Food, Agriculture and Forests

In agriculture, a significant amount of yield is lost due to weed plants that compete for nutrients, sunlight and water with the crops. Therefore monitoring of weed patches is a critical step within the agricultural production chain. A substantial amount of pesticides are sprayed onto fields, which have devastating negative effects on the environment. Precision Farming offers an unprecedented opportunity to automate and optimize processes in agriculture to manage agricultural fields more precisely based on their needs and lower the amount of pesticides.

The high-level goal of the project “Evaluation and Development of modern methods of Artificial Intelligence for Automatic  Weed Detection in Sorghum using drones (EWIS)”, which is funded by the Bavarian State Ministry for Food, Agriculture and Forests (Bayerisches Staatsministerium für Ernährung, Landwirtschaft und Forsten), is to develop a resource-saving and efficient measure for weed control that is cheaper and faster than conventional weed management.

UAV images analyzed using state-of-the-art deep learning methods to generate a classification mask of sorghum and weed patches: The left picture shows the capturing of images on the field, the right one a classification of sorghum (green) and weed (orange).

Innovative approaches and modern machine learning based methods will be investigated and developed to automatically and precisely detect various types of weed on agricultural fields. For this purpose, the growth of Sorghum – a energy plant used mainly in the production of biogas – is captured by unmanned aerial vehicles (UAV). These captures are then analyzed using modern machine learning methods (e.g. state-of-the-art deep learning architectures) to extract high level features. The methods are then applied to detect weed patches in future captures. Finally, the developed method will also be applied and evaluated on Zea mays to estimate its generalization abilities with respect to other cultivars. With this approach we might be able to reduce the usage of pesticides.

 

Project Information:

Project title: Evaluation and development of modern methods of artificial intelligence for automatic weed detection in sorghum using drones

Funding: Bayerisches Staatsministerium für Ernährung, Landwirtschaft und Forsten

Project partner: Michael Grieb, Raymond Ajekwe, Technologie- und Förderzentrum TFZ; Dr. Wouter Vahl‚ Dr. Jennifer Groth, Institut für Pflanzenbau und Pflanzenzüchtung (IPZ) der Bayerischen Landesanstalt für Landwirtschaft (LfL)

Funding ID: G2/N/19/13

 

 

Kontakt

Professorship Bioinformatics

Petersgasse 18
94315 Straubing

Head

Prof. Dr. Dominik Grimm

Phone: +49 (0) 9421 187-230
Fax: +49 (0) 9421 187-285
E-Mail: dominik.grimm@hswt.de

Team Assistent

Ingrid Meindl

Phone: +49 (0) 9421 187-271
Fax: +49 (0) 9421 187-285
E-Mail: ingrid.meindl@hswt.de