Abstract
This study investigates the use of exploratory data analysis and supervised learning techniques to analyze plant phenotyping traits, with a specific focus on: i) genetic diversity (wild type vs mutant tomato plants); ii) plant-plant interactions (primed vs non-primed plants using volatiles emitted by other stressed plants); and iii) plant stress response (using drought stress and comparing droughted plants with controls). The analyzed data consisted of high-throughput imaging at multiple wavelengths, which allowed for the examination of various morphological traits. The dataset contained the phenotypic characteristics of both wildtype and mutated tomato plants exposed to water stress. Machine learning algorithms were used to identify significant phenotypic indicators and predict plant stress responses. The use of techniques such as K-means clustering and Bayesian classifiers provided valuable insights into the temporal dynamics of plant traits under a variety of experimental conditions. This research emphasizes the importance of employing advanced statistical and machine learning methods to improve the precision and efficacy of phenotypic analysis in plant sciences.