Eleni Kalogeropoulou
Papers
2
Total Citations
12
H-Index
2
About
Eleni Kalogeropoulou is a pioneering researcher at the intersection of precision agriculture, plant pathology, and artificial intelligence. Her work focuses on developing non-invasive, AI-driven methods for the early detection of devastating fungal diseases, particularly grey mould caused by *Botrytis cinerea*. Kalogeropoulou’s major contribution lies in fusing multi-spectral imaging with advanced machine learning classifiers, such as image transformers, to identify infections before visible symptoms appear. Her 2024 paper on tomato grey mould detection, which has already garnered 8 citations, demonstrates how this approach can be integrated into autonomous agricultural robots. She has further validated the technique on pepper plants in a 2025 study (4 citations), showing its versatility across economically vital crops. By comparing her AI-based spectral imaging against molecular PCR assays, she has proven that computer vision can rival lab-based diagnostics in both speed and accuracy. This work directly supports global food security by enabling real-time, field-level disease management, reducing the need for blanket fungicide application. Kalogeropoulou’s research is a critical step toward fully autonomous, data-driven farming systems.
Research Focus
Key Achievements
Top Papers
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- 2