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.
  • Bulletin Board
  • description

    Please note:

    • Admission Notification
    • • Only those candidates whose GATE Registration Number appears in the waiting list are eligible for spot admissions
    • • All ST candidates who have applied for MTech in GNR (even if they did not appear in the interview) are eligible for the one (1) seat under ST Category available for spot admissions

    Posted on Jul 21, 2025