In this project, we use a cutting-edge microscope, a light sheet fluoresence microscope (or LSFM) to image cellular anatomy as plants grow. A key challenge with LSFM time series data is the huge volume of data produced. This requires new analysis methods to permit us to gain biological insight. One such problem is identifying formative divisions that give rise to anatomical patterns in 3D datasets of roots resolved over time. This project seeks to automatically identify particular anatomical changes in big datasets. With LSFM it is possible to acquire gigabytes of data per minute. Time course experiments can easily generate tens of gigabytes of data. This turns visualisation and analysis into a bottleneck. We will use machine learning approaches to allow new software to identify regions of interest within these datasets. We will build visualisation tools which will use the results of these approaches to allow biologists to navigate the data in meaningful ways, rather than blindly moving through the whole dataset. We will use these tools to investigate how root anatomy is altered in plants grown in low nutrient environments.