The project of computational modeling explores the potential of using different types of machine learning such as supervised and unsupervised machine learning to simulate plant behavior. Datasets composed of images, cartesian 3D coordinates, chemical-related data, and molecular data are exploited to gain a further understanding of the cognitive abilities in plants. 

Outcomes will be useful to start delineating novel and unique tools for agricultural practices of stress management by incorporating plant cognition to improve integrated crop management under field conditions.