Machine Learning in Agriculture Course | Soil & Crop Management | IIT Kharagpur
Course Details
| Exam Registration | 603 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Agricultural and Food Engineering, Integrated Soil and Water Management |
| Credit Points | 3 |
| Level | Undergraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 19 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Revolutionizing Agriculture: A Comprehensive Course on Machine Learning for Soil and Crop Management
The future of farming is intelligent, data-driven, and sustainable. To prepare for this technological revolution, the Indian Institute of Technology Kharagpur offers a groundbreaking 12-week course: Machine Learning For Soil And Crop Management. Designed for undergraduate students, this course bridges the gap between cutting-edge AI technology and traditional agricultural practices, empowering the next generation of agriculturists and engineers.
Meet Your Instructor: A Pioneer in Sensor-Based Soil Science
Leading this transformative course is Prof. Somsubhra Chakraborty, an esteemed Assistant Professor in Soil Science at IIT Kharagpur's Agricultural and Food Engineering Department. With a robust academic background including a PhD from Louisiana State University, USA, and post-doctoral research at West Virginia University, Prof. Chakraborty brings world-class expertise. His research focuses on the innovative use of proximal sensors and machine learning for soil management, a dedication reflected in his approximately 80 international journal publications and his role on the editorial board of Geoderma.
Who Should Enroll in This Course?
This course is meticulously designed for undergraduate students passionate about integrating technology with agriculture. It is particularly relevant for:
- Students of Agricultural Engineering
- Students of Agriculture and related fields
- Students of Environmental Science
Industry Support: The curriculum is highly valued by soil and crop testing services, soil remote sensing solution providers, and AI-based agricultural startups, ensuring the skills you learn are directly applicable to the modern agri-tech sector.
Course Overview: Blending AI with Agri-Science
The primary objective of this course is to move beyond traditional farming methods by developing eco-friendly, high-productivity systems. Over 12 weeks, students will explore the powerful applications of Machine Learning (ML) and Deep Learning (DL) in creating integrated, advanced soil and crop management systems. You will gain hands-on knowledge in:
- Machine Learning & Deep Learning fundamentals for agriculture
- Digital Soil Mapping techniques
- Image processing for soil and crop analysis
- Utilizing data from portable and proximal sensors
Detailed 12-Week Course Curriculum
| Week | Topic |
|---|---|
| Week 1 | General Overview of ML and DL Applications in Agriculture |
| Week 2 | Basics of Multivariate Data Analytics |
| Week 3 | Principal Component Analysis and Regression Applications in Agriculture |
| Week 4 | Applications of Classification and Clustering Methods in Agriculture |
| Week 5 | Diffuse Reflectance Spectroscopy: Basics and Applications for Crop and Soil |
| Week 6 | Use of ML for Portable Proximal Soil and Crop Sensors |
| Week 7 | ML and DL for Soil and Crop Image Processing |
| Week 8 | UAV and ML Applications in Agriculture |
| Week 9 | Hyperspectral Remote Sensing and ML Applications in Agriculture |
| Week 10 | Digital Soil Mapping – General Overview |
| Week 11 | Digital Soil Mapping with Continuous Variables |
| Week 12 | Digital Soil Mapping with Categorical Variables |
Essential Reading Materials
To complement the lectures and practical sessions, the course recommends two foundational texts:
- Introduction to Multivariate Statistical Analysis in Chemometrics by Kurt Varmuza and Peter Filzmoser
- Using R for Digital Soil Mapping by Malone, Minasny, and McBratney
Why This Course is Essential for Future Agri-Professionals
Agriculture stands at the cusp of a digital transformation. This course provides the critical toolkit to be at the forefront of this change. By understanding how to apply ML algorithms to sensor data, satellite imagery, and soil samples, you will learn to make precise predictions about soil health, optimize crop yields, and manage resources sustainably. Whether your goal is to join an innovative agri-startup, contribute to large-scale precision farming, or pursue advanced research, this course offers the perfect foundation. Enroll today and become a architect of the future of farming.
Enroll Now →