Skoltech Digital Agro
Introduction to Digital Agriculture

6 credits, Term 3

About our classes
The agriculture and food sector is facing multiple challenges. With the global population projected to grow from 7.6 billion in 2018 to over 9.6 billion in 2050, there will be a significant increase in the demand for food. At the same time, the availability of natural resources such as fresh water and productive arable land is becoming increasingly constrained. This will require an urgent transformation of the current agrifood system. Digital innovations and technologies may be part of the solution. The so-called 'Fourth Industrial Revolution(Industry 4.0) is seeing several sectors rapidly transformed by 'disruptive' digital technologies such as the Internet of Things, Artificial Intelligence and Computer Vision.
This course covered several milestone problems for future agriculture, like:
- data science for detection of harmful plants, and/or yield prediction;
- plants grow prediction and plant recognition for greenhouses.

Full Syllabus

Course available for Ms and PhD students
Course team
Maria Puklachik
Dmitriy Shadrin
Teaching Assistant
Polina Tregubova
GitHub Page
Materials and lectures are available on the GitHub course page
Our classes (Term 3)
Last presentations updates 2021
01. Introduction to Digital agriculture

General overview about problems, scopes and issues for digital agro in the World. We also present the key digital technologies with market sizes and start-up's maps.

The pdf available due the link

02. Data and basic statistics in agriculture and environmental sciences
  • What is the agriculture?
  • What kind of data usually supports agricultural studies, examples of R&D projects.
  • Common objectives and tasks of the agriculture: today and the future.
  • Pre-processing of data, description and tools for primary analysis remember the basics.

The pdf available due the link
03. ML and AI as
workhorses in the digital agriculture
  • AI, ML, and DL. What are this terms mean?
  • The map of the machine learning world.
  • Application of ML in Agriculture
  • The learning process.
  • State of the Art Application of ML in Agriculture and Environmental cases

The pdf available due the link

04. Crop yield models and sensitivity indexes
  • Climate change
  • Basics of Agricultural System Models:

-Statistical models

-Process-based models

-Data-driving approach

The pdf available due the link

05. Plant phenotyping
  • Crop Phenotyping or Crop Phenomics
  • Defining, limiting and reducing factors for crop
  • What we could measure?
  • Different spectra used in crop phenotyping
  • The modelling process that converts raw data into strongly model

The pdf available due the link

06. ML for plant growth dynamics prediction
3 ways of modeling:
  • Bottom-up modeling
  • Mixed: bottom up and data-driven modeling
  • Data-driven modeling

The pdf available due the link
07. Remote sensing and satellites in agriculture

General overview and practical cases


The pdf available due the link