PhD Student

Research

I am a researcher working on the subproject (SP) 2 of the MultiStress Research Unit 6101, which is examining the stress interactions in maize. My research focuses on understanding the physiological and biochemical responses of maize to concurrent biotic and abiotic stresses, with particular emphasis on drought, nitrogen deficiency, stem borer infestation, and Northern Corn Leaf Blight (NCLB). In natural and agricultural environments, crops are rarely exposed to a single stress at a time; instead, multiple stresses can occur simultaneously and interact in complex ways, potentially producing responses that differ substantially from those induced by individual stresses. My research therefore takes a multistress perspective, aiming to understand how maize perceives, responds to, and adapts to combinations of environmental and biological stressors.

To investigate these complex interactions, I employ an integrated combination of plant physiology, biochemistry, and high-throughput phenotyping approaches. I use hyperspectral imaging and thermal imaging as non-invasive tools to capture stress-induced changes in plant spectral and thermal characteristics. These imaging-based approaches are complemented by physiological and biochemical analyses, including measurements of stress-related enzymatic activities and antioxidant responses, to characterize the underlying mechanisms associated with maize responses to individual and combined stresses. By combining these complementary approaches, my research seeks to link observable phenotypic changes with the physiological and biochemical processes occurring within stressed plants. A central aspect of my research is the integration of multimodal datasets generated from imaging, physiological, and biochemical measurements. I aim to identify reliable phenotypic and biochemical indicators of stress, characterize stress-specific response patterns, and determine whether individual stresses and their combinations can be distinguished based on their physiological, spectral, thermal, and biochemical signatures. Particular emphasis is placed on understanding how maize responses change when stresses occur simultaneously and on identifying traits that may serve as potential stress biomarkers.

The study aims to translate complex physiological, biochemical, and imaging datasets into practical diagnostic and predictive tools by incorporating machine learning and data-driven approaches. These approaches will be used to develop models for stress classification, early stress detection, prediction, and biomarker identification. By integrating imaging-derived features with physiological and biochemical traits, the study seeks to develop robust predictive models capable of distinguishing individual stresses from combined stress conditions and detecting stress responses before visible symptoms appear. Ultimately, this research will contribute to the development of non-invasive, high-throughput, and data-driven approaches for crop stress phenotyping, enhancing the understanding of maize resilience under multistress conditions. The findings will support improved early stress diagnosis, crop monitoring, and development of stress-resilient agricultural practices under increasingly variable environmental conditions.

Selected Publications

Nxumalo, G. S., Ramabulana, T. S., Dlamini, Z., Louis, A., & Nagy, A. (2026). Integrating OPTRAM and machine learning with multimodal EO proxies for optimized irrigation scheduling in smallholder systems: a Vhembe District case study. Frontiers in Agronomy, 7.
DOI: 10.3389/fagro.2025.1697188.

Louis, A., Fehér, Z. Z., János, T., & Nagy, A. (2025). A new approach in monitoring regional water use efficiency in response to climate variability: a case study in Hungary. Journal of Agriculture and Food Research, 102497.
DOI: 10.1016/j.jafr.2025.102497

Tamás, J., Louis, A., Fehér, Z. Z., & Nagy, A. (2025). Land Cover Mapping Using High-Resolution Satellite Imagery and a Comparative Machine Learning Approach to Enhance Regional Water Resource Management. Remote Sensing, 17(15), 2591.
DOI: 10.3390/rs17152591

Angura, L., Bimantara, D. S., Magyar, T., Bódi, E. B., & Fehér, Z. Z. (2024). Spatio-temporal decision uncertainty of selected soil physical parameters can enhance variable rate irrigation. Columella Journal of Agricultural and Environmental Sciences, 11(1), 5–17. https://doi.org/10.18380/szie.colum.2024.11.1.05.
DOI: 10.18380/szie.colum.2024.11.1.05

Additional Information

Researchgate: https://www.researchgate.net/profile/Angura-Louis
Google scholar: https://scholar.google.com/citations?user=mJSWEKgAAAAJ&hl=en
ORCID: https://orcid.org/0009-0000-9724-8587
LinkedIn: https://www.linkedin.com/in/angura-louis