Ariane S.C. HOUETOHOSSOU

Doctorant

Research interest: Artificial Intelligence, Data Science, Algorithms optimisation, Multivariate linear mixed effect models, Univariate descriptive Statistics, Univariate Inference, forest inventory, ethnobotanics

Position

PhD Student

Home institution

University of Abomey-Calavi

Background

PhD student in Biometry at the Doctoral School of Agronomic and Water Sciences of the University of Abomey-Calavi, 2020:2023

MSc degree in Biostatistics on the topic: “using of the multivariate longitudinal data analysis to assess growth patterns of maize (Zea mays) in Benin, 2018

Bachelor degree in Agronomy with a specialization in Natural Resources Management, 2015

Research project (Max 200 words): Wastage of agricultural yield is almost 40 % due to lack of field monitoring, non-identified like disease. To optimize agricultural production, it is important to early detect plants diseases and apply adequate treatment to them. The question of precision agriculture should start with the most used agricultural products. Tomato (Solanum lycopersicum) is one of the most consumed vegetable in the world. In Benin, it is one of the most economically important vegetable crops and its production represents more than 51% of the total production of vegetable crops. For my Ph.D. research, I am working on artificial intelligence methods optimization to improve tomato yield by early detection of its diseases.

Awards and distinctions

  • 2020-2021 DAAD full scholarship for PhD in Biometry
  • 2016-2018: WAAPP/PPAAO-BENIN (West African Agricultural Productivity Program/ Programme de Productivité Agricole en Afrique de l’Ouest) scholarship for Master study in Biostatistics,
  • 2012-2015: Full national scholarship for agronomic studies
  • 2009: prize of first woman at the BEPC exam, Abomey

Total number of publications (Articles, communications, technical documents)

01

Publications

  1. Honfo SH, Houetohossou A, Assogbadjo A, Glele Kakaï RL, 2019. Effective use of statistical tools in agricultural sciences: a critical review of multivariate modelling methods. Journal of applied Biosciences 142: 14529-14539