Building AI-Powered Models for Sustainable Agriculture

SKILLS: Agricultural Data Engineering | Computer Vision | Decision Intelligence

Course Fee - ₹ 1500

Registration Closed

Overview

The course will focus on developing hands-on expertise in applying Artificial Intelligence and Data Science techniques to address real-world agricultural sustainability challenges. It enables learners to work with diverse agricultural datasets-from IoT sensors, drones, and satellite imagery-to design intelligent models for crop health monitoring, yield prediction, and resource optimization. Emphasizing the integration of computer vision, machine learning, and decision-support systems, the course equips participants to create data-driven, scalable, and eco-sensitive AI solutions that contribute to the achievement of Sustainable Development Goals (SDGs) in agriculture.

START DATE

Jul 01 2026

END DATE

Oct 15 2026

Delivery Mode

Online

Duration

15 Hours

Type

Theory

Course Fee

₹ 1500

Credentials and Certification

1 Credit

Course Category Micro-Credentials (Advanced)

TARGET AUDIENCE CHRIST University students

Digital Credential

indicating verified micro-credential completion.

RECOMMENDED FOR

Students exploring industry-relevent skills

Professionals planning to upskill and reskill

Faculty building digital deployment literacy

COURSE DELIVERY

Action-based learning materials with guided assessments and faculty support.

Course Outcomes

Our curriculum matches modern standard practices to provide exceptional training milestones.

Agricultural Data Integration

Integrate multisource agricultural data to build reliable AI-ready datasets that support precision farming and sustainable agricultural decision-making.

DATA MANAGEMENT

AI for Smart Agriculture

Understand how artificial intelligence enables data-driven farming by improving productivity, efficiency, and sustainability across agricultural systems.

CORE KNOWLEDGE

Deep Learning Applications

Apply deep learning techniques to identify crop health patterns, resource stress, and productivity trends using agricultural datasets.

MACHINE LEARNING

Computer Vision for Crop Monitoring

Utilize computer vision models to analyze field images, detect crop conditions, and support timely agricultural interventions with greater accuracy.

COMPUTER VISION

AI-Powered Decision Support

Develop intelligent decision-support systems that optimize agricultural inputs and improve resource utilization for sustainable farming practices.

AI SOLUTIONS

Sustainable Farming Innovation

Create AI-powered prototypes that promote precision agriculture, enhance operational efficiency, and support environmentally responsible farming practices.

SUSTAINABILITY

ELIGIBILITY CRITERIA

Final Year UG, PG, Research Scholars, Alumni, Academicians specializing in Data Science, Statistics, Computer Science and allied disciplines

Course Instructor

Expert guidance from acclaimed industry professional leaders.

Dr Kavitha R

Assistant Professor

Dr. Kavitha R has over 21 years of teaching experience and 11 years in research. She holds an MCA, MPhil, PGDHET, PhD, and Postdoc from the University of Mysore. She has cleared the UGC-NET and KSET exams in Computer Science and Applications. Dr. Kavitha completed a course in Data Mining from IISc Bangalore, earning an Outstanding Grade. Her research areas include Machine Learning, Pattern Recognition, and Data Science. She has developed real-time solutions for computer vision and carried out interdisciplinary research through computer-based models. Her research achievements include granted patents, published patents, and multiple publications in reputed journals like Scopus, IEEE, and Elsevier. She has authored a book titled 'Machine Learning with Python'. Dr. Kavitha is also a member of various professional bodies like GSTF Singapore, IDES, and IACSIT. She actively serves as a reviewer for leading computer science journals.

Program Overview

Academic Curriculum

A meticulous, guided learning path engineered to transform your cloud engineering expertise.

Module 1

Principles of Sustainable Agriculture

Module 2

Agricultural Data Preparation and Visualization

Module 3

Computer Vision for Sustainable Agriculture

Module 4

Learning Models for Sustainable Agriculture

Module 5