Skoltech Digital Agro
Course
Introduction to Digital Agriculture
6 credits, Term 3
About our classes
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
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
Instructor
Dmitriy Shadrin
Teaching Assistant
Polina Tregubova
Assistant
Our classes (Term 3)
Last presentations updates 2021
Last presentations updates 2021
1
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
2
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
3
03. ML and AI as
workhorses in the digital agriculture
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
4
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
5
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
6
06. ML for plant growth dynamics prediction
3 ways of modeling:
The pdf available due the link
- Bottom-up modeling
- Mixed: bottom up and data-driven modeling
- Data-driven modeling
The pdf available due the link
7
07. Remote sensing and satellites in agriculture
