BexBioTec
Institute for Plant Diagnostics
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Research and Current Projects
InnoHLig - Innovative humic substance concentrates from lignin | Sustainable alternatives for soil improvement
Project duration: 2024–2027 | Funded by ERDF/JTF NRW | Partners: Ruhr University Bochum, RWE Power AG
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InnoHLig is developing a process to produce high-quality humic substance concentrates from renewable, lignin-rich residues. The aim is to replace fossil lignite-based products while improving long-term soil fertility. The resulting humic substances are intended to support humus formation, soil regeneration, and reduced fertiliser use. Learn more.
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Our contribution: We test the efficacy of the developed substances under controlled laboratory conditions and assess their effects on plant growth and soil fertility. This helps determine whether the new lignin-based products can replace or outperform existing solutions.
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WATER - Subsurface irrigation, nano-fertilization and stress test kit for medicinal cannabis | Integrated, resource-saving cultivation system
Project duration: 2023–2026 | Funded by ZIM | Partners: University of Hohenheim, B+H Solutions, Ecotube Germany
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WATER is developing a standardised, integrated system for professional cannabis cultivation in greenhouses and polytunnels. The system combines subsurface irrigation, nano-fertilisation, optimised genetics, and a novel stress monitoring kit to deliver consistent yields and stable cannabinoid profiles.
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Our contribution: We are developing a stress testing kit in the form of an easy-to-use rapid antigen test. It measures plant stress levels directly on site and helps guide the targeted application of stress to optimise quality and compound profiles.
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Past Research Projects
Algae Fingerprint - AI-based rapid test for algae-based biostimulants | Quality control and efficacy prediction with artificial intelligence
Project duration: 2022–2023 | Funded by React-EU / EFRE.NRW | Partner: University of Münster
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The Algae Fingerprint project aimed to better understand the effects of algae-based biostimulants and to enable reliable prediction of their efficacy using an AI model. Since the composition of algae products can vary significantly, rapid and reliable methods are needed to ensure their quality.
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Our contribution: We comprehensively analyzed and characterized the algae-based preparations. Our data formed the basis for the development of an AI system that will enable fast and precise quality assessments in the future, thus strengthening the use of effective biostimulants in sustainable agriculture.
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