Brahim Benmouna
Papers
1
Total Citations
28
H-Index
1
About
Brahim Benmouna is a researcher specializing in agricultural engineering and food quality assessment, with a focus on non-destructive sensing technologies. His work integrates image processing, spectroscopy, and machine learning to develop innovative methods for evaluating fruit ripeness and quality. His most-cited paper, "Estimation of different ripening stages of Fuji apples using image processing and spectroscopy based on the majority voting method" (2020, 28 citations), exemplifies his contribution to precision agriculture. In this study, Benmouna and his team combined visual and spectral data with a majority voting algorithm to accurately classify apple ripening stages, offering a rapid, non-invasive alternative to traditional destructive testing. This approach has practical implications for reducing post-harvest losses and optimizing supply chain management. Benmouna’s work is notable for its interdisciplinary nature, bridging computer vision, chemometrics, and horticulture. His research has been cited by peers exploring similar sensor-based quality control systems, underscoring its relevance in the growing field of smart agriculture. By advancing automated fruit grading, Benmouna contributes to more efficient and sustainable food production systems.
Research Focus
Key Achievements
Top Papers
- 1