Tesi etd-11242025-184742 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
PALLAORO, MARTINA
URN
etd-11242025-184742
Titolo
AI-driven identification of the genetic determinants of stomatal features in maize (Zea mays L.)
Dipartimento
BIOLOGIA
Corso di studi
BIOTECNOLOGIE MOLECOLARI
Relatori
relatore Prof. Dell'Acqua, Matteo
Parole chiave
- AI
- drought
- GWAS
- maize
- stomata
Data inizio appello
15/12/2025
Consultabilità
Completa
Riassunto
Maize is one of the most important crops of the world, yet its cultivation is challenged by increased intensity and frequency of drought events, fuelled by the climate crisis. Several physiological and structural mechanisms can contribute to maize capacity to cope with water stress. A pivotal role is played by stomata, tiny pores on leaf surfaces that control gas exchange and water transpiration. The number of stomata per area, or stomatal density (SD), can vary between maize genotypes, hence affect their capacity for transpiration. Understanding the genetic basis of SD may facilitate the development of varieties with enhanced capacity to adapt to drought. In this study, we phenotyped maize leaves from 281 maize genotypes belonging to a maize population. We collected more than 8000 images using a handheld microscope on 2018 leaves sampled from seedlings grown in a growth chamber. We then used an AI-based detection method to recognize and count stomata, and integrated this data with the extensive genomic characterization of the population to perform quantitative genetics identifying loci that influence SD. We detected several significant associations across the genome and discuss the potential candidate genes. Our results show that natural allelic variations exist for stomatal density in maize, enabling us to shed light on the genetic determinants of this trait.
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| Nome file | Dimensione |
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| Tesi_Mar...laoro.pdf | 2.01 Mb |
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