LemnaTec Training

Machine Learning with LemnaGrid and LemnaExperiment

In class 6 we introduce using machine learning for phenotypic analyses. Machine learning is used to address complex analysis tasks. This can comprise specific phenotypic traits such as root system architecture, seed germination, or plant disease symptoms. Moreover, machine learning can be used to analyze images with low contrast, overlapping sample parts, or other features that are difficult to address with classical image processing.

6 Machine Learning for Root Analysis First Training of Machine Learning Model

First steps to establish machine learning models in root analysis. Selecting and labelling of training images and starting the training process.

6 Machine Learning for Root Analysis Part 2: Training Round 2+3 of ML Model

Evaluating the results of the first training. Conducting further training rounds to optimize the analysis results.

6 Machine Learning for Root Analysis: Creating ML Models in LemnaGrid directly

Machine learning in the ML Segmentation device of LemnaGrid. Creating and training models using labeled images.

6 Machine Learning – Using the Label Tool

Using the Label Tool for image labelling in machine-learning based analyses