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2 results on '"Mouad Zine-El-Abidine"'

1. Ordinalysis: Interpretability of multidimensional ordinal data

Author

Mouad Zine-El-Abidine, Helin Dutagaci, and David Rousseau

Subjects
Ordinal data, Pre-model interpretability, Dimension reduction, Feature space, Ordinal metrics, Computer software, QA76.75-76.765
Abstract

Ordinalysis is a software that enables dimension reduction, visualization and quantitative ordinality analysis of ordinal data. It is provided as a standalone executable file with a video tutorial. Applications of the software are shown on an ordinal synthetic dataset, ordinal real feature spaces and an ordinal image dataset. Ordinalysis allows the detection of violations of ordinality, the selection of the dimension reduction to preserve ordinality best or the demonstration of existing semantic ordinality in a set of raw data.

Published
2023
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2. Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves

Author

David Rousseau, Sabine Merdinoglu-Wiedemann, Mouad Zine el Abidine, Helin Dutagaci, Pejman Rasti, Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d'Angers (UA), INRAE - Université de Strasbourg, and Department of Big Data and Data Science, école d’ingénieur informatiqueet environnement (ESAIP)

Subjects
0106 biological sciences, Melon, Computer science, [SDV]Life Sciences [q-bio], Machine learning, computer.software_genre, 01 natural sciences, Article, Plant variety, Plant disease, ComputingMilieux_MISCELLANEOUS, business.industry, food and beverages, 04 agricultural and veterinary sciences, Plant phenotyping, Classification, Visual inspection, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Precision agriculture, Artificial intelligence, business, computer, Powdery mildew, 010606 plant biology & botany
Abstract

Precision agriculture faces challenges related to plant disease detection. Plant phenotyping assesses the appearance to select the best genotypes that resist to varying environmental conditions via plant variety testing. In this process, official plant variety tests are currently performed in vitro by visual inspection of samples placed in a culture media. In this communication, we demonstrate the potential of a computer vision approach to perform such tests in a much faster and reproducible way. We highlight the benefit of fusing contrasts coming from front and back light. To the best of our knowledge, this is illustrated for the first time on the classification of the severity of the presence of a fungi, powdery mildew, on melon leaves with 95% of accuracy.

Published
2020
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