GNR 703 Plant Phenomics Lab

  • Basics of R programming: Variables, data types, functions, R data structures, basic data transformation including handling/formatting time series data.
  • Data importing, preprocessing and visualization in R.
  • Basics of Python programming: Variables, data types, functions, Python data structures, basic data transformation including handling/formatting time series data.
  • Data importing, preprocessing and visualization in Python.
Case Studies (including but not limited to):
  • Phenotypic Trait Analysis in Crop Breeding: Analyzing phenotypic traits of different crop varieties to identify high-yielding varieties.
  • Plant Stress Detection: Detecting and quantifying plant stress using RGB, multispectral or hyperspectral imagery.
  • Mapping crop-health across fields: Applying machine learning algorithms to classify stress levels and visualize spatial patterns.
  • Time Series Analysis of Plant Growth: Time series visualization, decomposition, and forecasting growth-related trait development.
  • Predictive Modeling of Crop Yield: Developing predictive models for crop yield based on phenotypic and environmental data.

This course aims to teach students how to develop full-fledged high-throughput plant phenotyping pipelines in R and Python, as well as maintain open-source code repositories on GitHub.

  • Text / References:
  • Wickham, Hadley, and Garrett Grolemund. R for data science. Vol. 2. Sebastopol, CA: O'Reilly, 2017.
  • Favero, Luiz Paulo, Patricia Belfiore, and Rafael de Freitas Souza. Data science, analytics and machine learning with R. Academic Press, 2023.
  • Winters, Ralph. Practical predictive analytics. Packt Publishing Ltd, 2017.
  • James, Gareth, et al. "Statistical learning." An introduction to statistical learning: With applications in Python. Cham: Springer International Publishing, 2023. 15-67.
  • McKinney, Wes. Python for data analysis: Data wrangling with pandas, numpy, and jupyter. O'Reilly Media, Inc., 2022.
  • Geron, Aurelien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media, Inc., 2022.