Artificial Intelligence for Agriculture Technology and Climate Change (online)


This course is for professionals in the agriculture, agtech, sustainability/climate change and food value chain sectors who aspire to be pioneers in the application of artificial intelligence (AI) in these domains.

The philosophy of the course is based on applying AI techniques to specific problems in agriculture and climate change, and exploring the broader impact of these technologies in the food value chain.  
You will gain a unique understanding of applying AI to complex, interdisciplinary problems based on data.  

The course emphasises AI for Good, and AI with cyber-physical systems, i.e., AI with Edge, systems thinking, and design thinking.  
You are expected to take an exploratory and data-driven mindset for the emerging domain. 

You do not need to know how to code, but some understanding and prior experience would be beneficial.

The course includes coding-based demonstrations and case studies for, including microclimate prediction, sustainability, what-if (causal) analysis, data fusion, analysing satellite data, food security, and food value chain. 

The course explores the impact of Artificial Intelligence on many aspects of agriculture and sustainability, including the following themes. Where possible, these themes will be covered in the context of the platform (including code).
See here for some further information on the potential of AI in agtech through the Microsoft Azure FarmBeats initiative.

  • Digital agriculture and the impact of AI 

    • Supply chain
    • Sustainability (carbon/water/climate) 
    • Integrated solutions for agriculture value chain 

    • Social innovation 

    • Small farm holdings (unique considerations) 

    • Processing of agricultural produce 

    • Pest control 

  • Management and market ecosystems for AI and AgTech 

    • Farm management and planning  

    • Data management for farms  

    • Water management for agriculture 

    • Soil management for agriculture  

    • Management of labour in farms 

    • Financing and insurance 

    • Market access, including marketplaces  

    • Crop marketing 

    • Trading platforms

    • Food recovery / preventing loss  

  • AI and AgTech Technologies 

    • Satellites and data management 

    • Microclimate  

    • Genomics 

    • Precision applications  

    • Imaging systems & services  

    • Robotics 

    • Sensors and IoT  

    • Controlled agriculture environment (vertical farming, hydroponics, etc.) 

  • Analytics 

    • Yield forecasting  

    • Farm data aggregators, integrators, analytics 

    • Agriculture metrics

This year, we are pleased to extend the course to Climate change and causal factors for agriculture. The course will use the book “Causal Inference and Discovery in Python” . We will work with the author, Aleksander Molak, to include specific causal machine learning use cases in the course. A copy of the book is included in the course fees.

Extending the existing partnership with ADT Baramati, we will implement a number of Causal AI use cases for agtech and climate change as course case studies. The code developed will be based on the book, and will be Open sourced. We will focus on causal algorithms for yield prediction and climate impact on crops. We will also develop data gathering methods for causal algorithms for agtech.

The course will also include new innovations from the platform including the use of LLMs for agtech and use of LEARN modules from farmvibes.

The course will develop a general format/ strategy for sample cases.

Potential causal case studies for AI in agtech include below:

  1. Crop Management and Yield Prediction
  2. Farm resource allocation
  3. Water management and drought prediction
  4. Soil management
  5. Crop rotation
  6. Pest and disease management
  7. Microclimate prediction
  8. Sustainable Agriculture practices
  9. Climate-Resilient Crop Breeding
  10. Forestry
  11. Carbon credits
  12. Supply chain
  13. Agriculture policy
  14. Designing incentives
  15. Behavioural change
  16. Impact of education
  17. Precision Agriculture

The above may be subject to minor changes and adjustments.

Dates, Times and Delivery

The Artificial Intelligence for Agriculture Technology and Climate Change (online) course will run over six sessions.

These six live video sessions will be held on Microsoft Teams at 15:00 – 18:00 (UK time) over a period of two weeks on Mondays, Wednesdays, and Fridays:

A world clock, and time zone converter can be found here:

Accessing Your Online Course 

Details about accessing the private MS Teams course site will be emailed to you during the week prior to the course commencing.  

Please get in touch if you have not received this information within three working days of the course start date. 

Programme details

Some of the themes covered in this course include the use of artificial intelligence in:

  • Autonomous systems: agricultural robots and drone monitoring systems, driverless tractors, automated sprinklers and self-harvesting machines etc.
  • Crop analysis: monitoring soil quality, promoting organic crops, monitoring weeds, precision agriculture, using drones for crop analysis
  • AI for Good
  • Sustainability and climate change
  • Yield and demand forecasting
  • Food tech/wider value chain including impact of blockchain
  • Technology deployment like sensors , Industry 4.0 etc
  • AI and agtech in the emerging markets
  • How to scale AI for agtech applications
  • Responsible AI in agriculture
  • Data sharing
  • Agtech entrepreneurship
  • Emerging market challenges  
  • Innovation in agtech

The primary implementation and data partner for this course is the Agricultural Development Trust Baramati and the programme implementing partner will be AIC-ADT Baramati Foundation (Innovation and Incubation centre)

NVIDIA Omniverse™ is a scalable, multi-GPU real-time reference development platform for 3D simulation and design collaboration. In this course, we will discuss and demonstrate how this technology can be used to model agriculture based phenomena including climate change. The course participants will be encouraged to develop their own extensions and applications for Omniverse based on climate change and agtech models.

Other partner companies and speakers will be announced soon.

Digital Certification

To complete the course, you will be required to attend and participate in all of the live sessions on the course in order to be considered for a certificate. Participants who complete the course will receive a link to download a University of Oxford digital certificate. Information on how to access this digital certificate will be emailed to you after the end of the course.

The certificate will show your name, the course title and the dates of the course you attended. You will also be able to download your certificate or share it on social media if you choose to do so.


Description Costs
Standard Course Fee £875.00


Fees include electronic copies of course materials.

All courses are VAT exempt.

Register immediately online 

Click the “book now” button on this webpage. Payment by credit or debit card is required.


Ajit Jaokar

Course Director and Tutor

Based in London, Ajit's work spans research, entrepreneurship and academia relating to artificial intelligence (AI) and the internet of things (IoT). 

Ajit is the Course Director and/or tutor for Continuing Education’s portfolio of artificial intelligence courses for professionals:

Ajit is also a Visiting Fellow in the Department of Engineering Science here at the University of Oxford.

He also works as a Data Scientist through his company feynlabs - focusing on building innovative early stage AI prototypes for domains such as cybersecurity, robotics and healthcare.

Besides the University of Oxford, Ajit has also conducted AI courses in the London School of Economics (LSE), Universidad Politécnica de Madrid (UPM) and as part of the The Future Society at the Harvard Kennedy School of Government.

He is also currently working on a book to teach AI using mathematical foundations at high school level. 

Ajit was listed in the top 30 influencers for IoT for 2017 along with Amazon, Bosch, Cisco, Forrester and Gartner by the German insurance company Munich Re.

Ajit publishes extensively on KDnuggets and Data Science Central.

He was recently included in top 16 influencers (Data Science Central), Top 100 blogs (KDnuggets), Top 50 (IoT central), and 19th among the top 50 twitter IoT influencers (IoT Institute). 

His PhD research is based on AI and Affective Computing (how AI interprets emotion).

Ms Ayshe Mutlu

Senior Course Tutor

Data Scientist

Ayşe Mutlu is a data scientist working on Azure AI and devops technologies. Based in London, Ayşe’s work involves building and deploying Machine Learning and Deep Learning models using the Microsoft Azure framework (Azure DevOps and Azure Pipelines).

She enjoys coding in Python and contributing to Open Source Initiatives in Python.

Yogesh Balasaheb Phatake

Course Tutor

Dr. Yogesh Phatake is an Assistant Professor in the Department of Microbiology, at Shardabai Pawar Mahila Arts, Commerce and Science College (Baramati, Maharashtra, India 413115).
The institution is run by Agricultural Development Trust, Baramati, one of India’s premier agricultural research and development organizations which is also the parent organization of Krishi Vigyan Kendra Baramati, India’s national and international award winning farm science centre. 

Dr. Yogesh completed his graduation and post graduation in Microbiology and has teaching and research experience of over ten years.

He also completed his Ph.D. in 2019.
His doctoral work was based upon therapeutic applications of biopigments (Prodigiosin) produced by bacteria Serratia marcescens.

He was recently awarded with the Ph.D. guideship by Savitribai Phule Pune University (Pune) and became one of the youngest research guides.
Yogesh has published total 23 research papers in multiple high-impact factor, peer reviewed national and international research journals of life sciences, agriculture microbiology, industrial microbiology etc.
He has also presented his research work in many national and international conferences and was awarded with the best oral and poster presentation awards. 

His passion for teaching different life science subjects motivates him to use his skills for the betterment of bright students coming from rural regions.
He frequently delivers guest lectures at different educational and research institutes across the country.

Yogesh likes to work in different fields of life sciences like Agriculture Microbiology, Fermentation technology, Plant-microbial interactions, Biofertilizers, Biofilms, Nanofertilizers, Adaptic stress management, Hydroponics etc. 

Chaithanya Bandi

Course Tutor

Chaithanya Bandi obtained his PhD in Operation Research from MIT and has been a faculty member at Kellogg School of Management at Northwestern University, National University of Singapore and Indian School of Business. He is currently a researcher in the Research for Industry team at Microsoft Research. He is broadly interested in the problems of decision making under uncertainty, incomplete information and risk with applications to Supply Chain and Operations Management. His methodological interests lie in leveraging knowledge graphs in the analysis and control of supply chain networks.  At Microsoft Research, Chaithanya has focused on the use of Large Language Models as a way to organize and understand data generated by supply chains.

Ranveer Chandra

Course Tutor

Managing Director, Research for Industry, Networking Research, CTO, Agri-Food, Microsoft

Ranveer Chandra is the Managing Director of Industry Research at Microsoft, where he is leading a team driving innovations across different industries. He also serves as the CTO of Agri-Food at Microsoft, and leads Networking Research in Microsoft Research, Redmond. Previously, he was the Chief Scientist of Microsoft Azure Global.

Ranveer's research has shipped as part of multiple Microsoft products, including VirtualWiFi and low-power algorithms in Windows 7, Windows 8, and Windows 10, Energy Profiler in Visual Studio, and the Wireless Controller Protocol in XBOX One.

Ranveer started the FarmBeats project at Microsoft Research in 2015, and has been leading it since then. He is also leading the battery research project, and the white space networking project at Microsoft Research. He was invited to the USDA to present his work on FarmBeats, including to the Secretary, this work was featured by Bill Gates in GatesNotes, and was selected by Satya Nadella as one of 10 projects that inspired him in 2017. Ranveer has also been invited to the FCC to present his work on TV white spaces, and spectrum regulators from India, China, Brazil, Singapore and US (including the FCC chairman) have visited the Microsoft campus to see his deployment of the world’s first urban white space network. As part of his doctoral dissertation, Ranveer developed VirtualWiFi. The software has been downloaded more than 750,000 times and is among the top 5 downloaded software released by Microsoft Research. It is shipping as a feature in Windows since 2009.

Ranveer has published more than 100 papers, and holds over 150 patents granted by USPTO. His research has been cited by the popular press, such as MIT Technology Review, The Economist, New York Times, WSJ, among others. He has won several awards, including best paper awards, and the MIT Technology Review's Top Innovators Under 35, TR35. He was recently recognized as one of America's 50 Most Disruptive Innovators by the Newsweek magazine. Ranveer has an undergraduate degree from IIT Kharagpur, India and a PhD from Cornell University.

Robin Cole

Course Tutor

Robin holds a PhD in physics from the University of Cambridge and has worked in a number of research and development roles within industry.  

He currently works as a senior data scientist at a satellite imagery company where he applies cutting edge deep learning techniques to remote sensing imagery.  

Robin has a decade of experience with the Python programming language and has expertise in the use of cloud computing infrastructure.  

Robin is a passionate advocate for the open source software movement and makes regular contributions online and has presented at Python conferences and appeared on podcasts. 

Renato Cunha

Course Tutor

Renato is a software engineer in the Research for Industry group at Microsoft Research. This means he works at the sweet spot between testing out research ideas and writing software that supports such research, bridging the gap between research models and industry problems.

Renato received his PhD degree in 2022, and his MSc degree in 2010, both in Computer Science, from the graduate program in Computer Science of UFMG.

Prior to joining Microsoft Research, he did research on Machine Learning for resource management, Machine Learning for digital agriculture, Machine Learning for community detection, hybrid HPC Cloud, and built infrastructure for genomic analytics pipelines in the Cloud.

Peeyush Kumar

Course Tutor

Peeyush Kumar is a senior research scientist at Microsoft Research. He is an accomplished systems and AI researcher with a focus on two key areas: a) building AI-supported social technologies for equitable and resilient communities, and b) utilizing large AI models to bridge people, processes, and technology in industrial systems, particularly food and energy. His core research expertise spans large language models (LLMs), reinforcement learning (RL), community psychology, community economics, cultural liberation, operations research (OR), and stochastic processes. By combining traditional and modern knowledge, he aims to develop social technologies that promote harmony, prosperity, and equity.

His research philosophy emphasizes the integration of people, processes, and technology in a regenerative and reproducible manner, driven by a passion for creating a better world with resilient and equitable communities and economies. Before joining Microsoft in 2019, He co-founded healthcare technology company Cohort Intelligence (now Engooden Health), where he served as CTO and managed the development of a chronic care management product. Additionally, He have explored psychological and cultural aspects of healthy family systems, resilient relationships and communities, and regenerative and circular economies.

Ishan Kumthekar

Course Tutor

Ishan studies computer science at the University of Florida.

He has collaborated with the causal AI team at  Microsoft and the ADT Baramati team to develop casual machine learning algorithms using the DoWhy library.
The solution will be open sourced on the farmvibes platform and uses causal inference to predict for crop yield of farm. The solution provides accurate adjustments for optimising crop yield and displaying crop parameters for ‘what-if’ scenarios i.e effect on yield if fertilizer increased by 5kg/acre etc.

Ishan has also applied causal machine learning to detect underlying belief systems for LLMs(large language models).
Given a certain text, the problem is to detect what belief system, i.e what fundamental propositions are the basis of reasoning for that text. Done by performing LDA for topic modelling and then detecting cause-effect pairs in text (i.e propositions).

Prof Nilesh Nalawade

Course Tutor

CEO of Agricultural Development Trust Baramati

Prof. Nilesh Nalawade currently serves as the CEO of Agricultural Development Trust Baramati, one of India’s premier agricultural research and development organizations which is also the parent organization of Krishi Vigyan Kendra Baramati, India’s national and international award winning farm science centre.  

Prof. Nalawade completed his post-graduate studies in animal sciences at the Wageningen University, Netherlands.  

He has a rich and varied experience in conceptualization and development of various successful bilateral initiatives such as starting the first international undergraduate programme at Baramati, India, in the agriculture sector in collaboration with Van Hall Larenstein University in the Netherlands, setting up of the Indo-Dutch Centre of Excellence for Vegetables, setting up of Centre of Excellence for Animal Genetic Improvement, etc. 

He has a natural affinity for technology and innovation and has implemented his ideas in these spaces by leading the setting up of the Atal Incubation Center ADT Baramati Foundation, India’s largest Innovation and Incubation Centre, where he mentors over 30 of the brightest start-ups and many more aspiring entrepreneurs and innovators from across India.  

He has received various national and international awards for his work such as the prestigious Rastriya Vidya Saraswati Pursaskar. He has also published multiple high-impact, peer reviewed research papers in the agriculture and animal sciences sectors and held a position as a Member of the Management Committee  of National Institute of Abiotic Stress Management Baramati. 

Andrew Nelson

Course Tutor

Andrew Nelson is a fifth-generation farmer and a software engineer who runs Nelson Farms, Inc. and Silver Creek Farms, Inc. in the Palouse region of eastern Washington, USA. He produces wheat, garbanzo beans, peas, lentils, canola, and barley on 7,500 acres of land. He is also a software engineer at University of Washington, a consultant for Ag Tech startups, and an early adopter of new technologies such as drones and sensors to improve efficiency, reduce input costs and boost yields on his farm. He has conducted hundreds of experiments and pilots with various technologies to optimize his farming operations and make better management decisions. He is passionate about precision agriculture and sustainability and believes that technology is the key to profitability and environmental stewardship.

Leonardo Nunes

Course Tutor

Leonardo Nunes is the director of engineering for Research for Industries at Microsoft Research where he is responsible for the team building FarmVibes.AI, a geospatial AI platform for Agriculture and Sustainability. He has been with Microsoft for the past 8 years, acting as both researcher and developer in research and product teams. Before his current position at Microsoft Research, he was a Principal Researcher at Azure Computer Vision team, building novel products on real-time computer vision. Leonardo has a Master’s and Doctorate degrees in Electrical Engineering from the Federal University of Rio de Janeiro, with a focus on signal processing for multimedia signals and machine learning.

Peder Olsen

Course Tutor

Peder Olsen is a Principal Research Scientist in Research for Industry at Microsoft Research, Redmond, WA, USA. His research focuses on AI, machine learning and computer vision applications of remote sensing. He is a senior IEEE member and prior to joining Microsoft Research, he was a Prinicipal Research Staff Member at IBM Research working in the speech recognition group. He received his Ph.D. degree in 1996 in mathematics from the University of Michigan, Ann Arbor.

Riyaz Pishori

Course Tutor

I am a principal program manager at Microsoft with over 30 years of experience in the software industry. I work closely with Microsoft researchers and engineers to bring innovations to industry verticals such as agriculture, energy, retail CPG and more.

I also led the development of platform solutions for AI and analytics using industry data.
In addition, I have been involved in various projects related to enterprise management, OSS component management and Windows updates.

Previously, I was a group program manager for Windows Live and Windows Features Platform, where I delivered routing delivery platform and showcased new Windows features via inbox applications in Windows 7.

Before that, I was a senior program manager for Windows Distributed Framework (RPC, COM, OLE, COM+, .net EnterpriseServices), where I shipped numerous features across multiple Windows and Windows Server and .net framework releases.

I started my career as a developer at IBAX, where I developed Windows doctor's office management software.
I have a master's degree in electrical and computer engineering with a focus on artificial intelligence from University of South Carolina and a bachelor's degree in electrical engineering from Veermata Jijabai Technological Institute (VJTI).

I am passionate about delivering high-quality products that solve real-world problems and create value for customers.

Swati Sharma

Course Tutor

Swati obtained her PhD from the University of Strasbourg, France.

During her time at Microsoft, she has been involved in developing data-driven approaches for agricultural applications, such as soil carbon modeling using causal methods and identifying sustainable farming practices.
These efforts also aim to enable better yield prediction using GXE data. 

Swati has had the opportunity to work on a variety of research areas in the past, including image processing and analysis approaches for geospatial and medical imaging applications, as well as teaching underprivileged and first-generation college students.
More recently, she has tackled problems in the autonomous driving space at HERE Technologies, developing scalable mapping and localization approaches for lane-level crowd-sourced data. 

Outside of work, Swati is passionate about autism awareness and assisting autistic adults in obtaining and retaining employment. 
Swati also enjoys hiking with her children and traveling.


If you would like to discuss your application or any part of the application process before applying, please click Contact Us at the top of this page.

Level and demands

If you're uncertain whether this course is suitable for your requirements, please email us with any questions you may have.

IT requirements

This course is delivered online using Microsoft Teams. You will be required to follow and implement the instructions we send you to fully access Microsoft Teams on the University of Oxford's secure IT network.

To participate you must be familiar with using a computer for purposes such as sending email and searching the Internet. You will also need regular access to the Internet and a computer meeting our recommended minimum computer specification.
It is advised to use headphones with working speakers and microphone.