GNR 702 Principles of Predictive Plant Phenomics

  • Basics of plant phenotyping: Plant traits, phenotypes, role of phenomics in precision agriculture.
  • Leaf and Stomatal Functions: Leaf temperature and energy fluxes, photosynthesis and stomal conductance.
  • Plant-Environment Interactions: Plant phenology, water and nutrient uptake in plants, biotic and abiotic stresses, stress tolerance mechanisms and phenotypic plasticity, and agronomic practices influencing yield potential.
  • Plant phenotyping sensors: Proximal and remote sensing methods, imaging techniques, HTP platforms, and types of HTP data.
  • Data Science fundamentals for high-throughput plant phenotyping: Data distributions, descriptive and inferential statistics, data visualization, preprocessing, transformations, and feature engineering.
  • Introduction to time-series data: Moving averages, exponential smoothing, ARIMA models, evaluating TS forecasts of plant phenology and trait development.
  • Computer Vision: Supervised and unsupervised learning of lab and field phenomics image data, model training, validation, and evaluation for different predictive phenomics applications.
  • Text / References:
  • Yumurtaci, Aysen, and Hulya Sipahi. "New Generation Plant Phenomics Applications for Next Generation Agricultural Practices." Agricultural Biotechnology: Latest Research and Trends. Singapore: Springer Nature Singapore, 2022. 415-431.
  • Thenkabail, Prasad S., John G. Lyon, and Alfredo Huete, eds. Fundamentals, sensor systems, spectral libraries, and data mining for vegetation. CRC Press, 2018.
  • Kumari, Pooja, et al. "Plant Phenomics: The Force Behind Tomorrow’s Crop Phenotyping Tools." Journal of Plant Growth Regulation (2024): 1-19.
  • Montgomery, Douglas C., Cheryl L. Jennings, and Murat Kulahci. Introduction to time series analysis and forecasting. John Wiley & Sons, 2.
  • Kelleher, John D., Brian Mac Namee, and Aoife D`arcy. Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT Press, 2020.
  • Torralba, Antonio, Phillip Isola, and William T. Freeman. Foundations of computer vision. MIT Press, 2024.