“Optimisation of Machine Learning Techniques performance for tomato yield prediction and Disease Detection under Simulated Climate and Infection Conditions.”
Country of Study
Benin
Institution
University of Abomey-Calavi
Expected Year of Completion
2025
Thematic Area
Climate Change, Natural Resources, Environment/ Agriculture
Education
Ariane’s PhD thesis is in Biostatistics. She holds an MSc in Biostatistics from the University of Abomey-Calavi. Currently a Teaching Assistant at the University of Abomey-Calavi, she is expected to complete her PhD studies there in 2025.
Research Summary
Ariane’s research aims to enhance tomato yield in Benin by applying artificial intelligence (AI) and machine learning (ML) techniques to analyze climate data, fertilizer usage, and disease occurrences, addressing the challenges posed by climate variability and disease pressures on tomato production. While AI and ML have been utilized in other regions for similar purposes, there is a notable absence of studies focusing on tomatoes in Benin’s unique climatic and agricultural context. This project seeks to identify critical climate factors affecting tomato yield, optimize agricultural practices through AI-driven predictions, and improve disease detection methods, ultimately supporting sustainable tomato cultivation in Benin.
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