Course Code Course Title Credits Type
Agricultural Informatics and Artificial Intelligence (AI) 3 (2+1)

Course Objectives (COs)

CO No. Description Bloom’s Level
CO1 Understand the fundamental concepts of computers, operating systems, internet technologies,
and IT tools relevant to agriculture.
(L2)
CO2 Apply data processing tools such as MS-Office, databases, and basic programming concepts
to solve agricultural information needs.
(L3)
CO3 Analyze the role of e-Agriculture, ICT tools, crop simulation models, geospatial technology,
and decision-support systems in modern farming.
(L4)
CO4 Evaluate Artificial Intelligence (AI), IoT, and Big Data applications for smart and sustainable
agricultural practices.
(L5)

Course Learning Outcomes (CLOs)

CLO No. Description Bloom’s Level
CLO1 Explain the basic concepts of computers, operating systems, memory, internet,
and their relevance in agricultural informatics.
(L1 – Remember/Understand)
CLO2 Use MS-Office, databases, internet tools, and basic programming concepts to
manage and analyze agricultural data.
(L3 – Apply)
CLO3 Examine ICT applications, e-Agriculture platforms, crop simulation models,
geospatial tools, and DSS for farm-level decision-making.
(L4 – Analyze)
CLO4 Assess the role of AI, IoT, Big Data, and smart devices in precision agriculture,
crop/livestock management, and food systems.
(L5 – Evaluate)

CO–PO Mapping Table

Scale: 3 = Strong, 2 = Moderate, 1 = Slight, “–” = No Contribution

CO / PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12
CO1 3 3 2 2 1
CO2 3 3 3 2 3 1 1
CO3 2 3 3 3 3 2 2 2
CO4 2 3 3 3 3 2 2 3 2

 

Bloom’s Taxonomy Levels Used

  • L1 – Remember
  • L2 – Understand
  • L3 – Apply
  • L4 – Analyze
  • L5 – Evaluate
  • L6 – Create

📘 COURSE SYLLABUS (THEORY)

🔷 MODULE 1: Fundamentals of Computers and Basic IT Tools

Unit Topic
1.1 Introduction to Computers
– Definition and characteristics of computers
– Applications of computers in agriculture
1.2 Anatomy of Computers
– Hardware components
– Input/output devices
– Peripheral devices
1.3 Memory Concepts
– Primary and secondary memory
– Units: bit, byte, KB, MB, GB, TB
1.4 Operating Systems
– Definition and functions
– Types: Windows, Linux, Android
1.5 MS Office Applications
– Creating, editing, formatting documents (MS Word)
– Data presentation & scientific documentation (PowerPoint)
– Spreadsheet analysis, graphs, formulas (Excel)

🔷 MODULE 2: Database Management, Internet & Programming Basics

Unit Topic
2.1 Database Concepts
– Database definition and types
– Creating databases
– DBMS in agriculture
– Agricultural information systems
2.2 Internet and WWW
– Concepts & components
– Browsers, search engines
– Web applications in agriculture
2.3 Basics of Computer Programming
– Logic, algorithms, flowcharts
– Standard input/output operations
– Relevance in agricultural data analysis

🔷 MODULE 3: ICTs, e-Agriculture, Crop Models, Geospatial & Decision Tools

Unit Topic
3.1 e-Agriculture
– Concepts, design & development
– ICT tools
– Mobile advisory systems
3.2 Crop & Climate Models
– Statistical, weather, crop models
– Calibration, validation
– Nutrient/irrigation computing tools
3.3 Automated and Smart Devices
– Sensors & automation
– Precision agriculture tools
3.4 Smartphone Applications
– Farm advisory apps
– Market & post-harvest apps
3.5 Geospatial Technologies
– GIS, GPS, Remote sensing
– Applications in agriculture
3.6 Decision Support & Expert Systems
– Soil information systems
– Crop calendar planning
3.7 Digital Agriculture Initiatives
– Digital India, AgriTech missions
– IDEA framework & policies

🔷 MODULE 4: Artificial Intelligence, IoT & Smart Agriculture

Unit Topic
4.1 Introduction to AI
– Definition, background, Turing test
– BFS, DFS, Best-first, A*
4.2 IoT & Big Data in Agriculture
– Sensors, IoT devices, Cloud platforms
4.3 AI Applications in Agriculture
– Crop & livestock monitoring
– Precision mapping & analytics
– Automated weeding/harvesting
4.4 Smart Agriculture Concepts
– Smart farming systems
– Future technologies in digital agriculture
Practical Syllabus
Unit Practical Title & Description
P1 Introduction to computer hardware components, input/output devices, and basic troubleshooting.
P2 Hands-on working with the Windows/Linux operating system: File management, system settings, and OS utilities.
P3 Working with MS-Word: Formatting, tables, charts, mail merge, and documentation.
P4 Microsoft Excel for agricultural calculations: Data entry, formulas, sorting, filtering, and basic statistical analysis.
P5 Spreadsheet-based demonstration of crop yield estimation, cost-benefit analysis, and farm record digitization.
P6 Internet tools in Agriculture: Email creation, research databases, government portals, and e-Agriculture platforms.
P7 Database management practical: Creating tables, forms, and queries using MS-Access or LibreOffice Base.
P8 Introduction to programming using Python: Variables, operators, loops, input/output operations.
P9 Simple Python script for agricultural applications: Rainfall trend analysis, pest-alert threshold demonstration.
P10 Demonstration of crop simulation models, GIS, and geospatial tools used in precision agriculture.

Theory Session Plan

Session No. Module Topic Sub-Topics Covered
Module 1: Fundamentals of Computers and Basic IT Tools (Sessions 1–6)
1 Module 1 Introduction to Computers Definition, Characteristics, Role in Agriculture
2 Module 1 Anatomy of Computers Hardware, Input/Output Devices, Peripheral Devices
3 Module 1 Memory Concepts Primary & Secondary Memory, Units (Bit, Byte, KB, MB, GB, TB)
4 Module 1 Operating Systems Definition, Functions, Windows/Linux/Android
5 Module 1 MS Word Document Creation, Editing, Formatting
6 Module 1 MS Excel & PowerPoint Graphs, Tables, Data Presentation, Statistical Analysis
Module 2: Database Management, Internet & Programming Basics (Sessions 7–12)
7 Module 2 Database Concepts Definition, Types, Creating Databases, Agri DBMS
8 Module 2 Agricultural Information Systems Types, Applications in Agriculture
9 Module 2 Internet & WWW Internet Concepts, Browsers, Web Applications in Agri
10 Module 2 Search Engines & Web Services Online Agriculture Tools
11 Module 2 Basics of Programming Logic, Variables, Input/Output
12 Module 2 Algorithms & Flowcharts Steps, Decision Making, Problem Solving
Module 3: ICTs, e-Agriculture, Crop Models, Geospatial & Decision Tools (Sessions 13–23)
13 Module 3 e-Agriculture Concepts ICT Tools, Mobile Advisory Systems
14 Module 3 Computer Models in Agriculture Statistical, Weather, Crop Simulation, Input/Output, Calibration, Validation
15 Module 3 Automated & Smart Devices Sensors, Automated Irrigation, Precision Tools
16 Module 3 Smartphone Applications Farm Advisory Apps, Market & Post-Harvest Tools
17 Module 3 Geospatial Technologies GIS, GPS, Remote Sensing, Agri Information Generation
18 Module 3 Decision Support & Expert Systems Soil Information Systems, Crop Calendar, Contingent Planning
19 Module 3 Digital Agriculture Initiatives Digital India, Agritech Missions, IDEA Framework
Module 4: Artificial Intelligence, IoT & Smart Agriculture (Sessions 24–30)
24 Module 4 Introduction to AI Definition, Background, Turing Test, Search Strategies (BFS, DFS, Best-first, A*)
25 Module 4 IoT & Big Data in Agriculture IoT Devices, Sensors, Cloud Platforms, Data Collection
26 Module 4 AI Applications in Agriculture – Part 1 Autonomous Crop Management, Livestock Health Monitoring
27 Module 4 AI Applications in Agriculture – Part 2 Precision Yield Mapping, Predictive Analytics, Automated Weeding & Harvesting
28 Module 4 AI Applications in Agriculture – Part 3 Sorting & Classification in Food Processing
29 Module 4 Smart Agriculture Concepts – Part 1 Smart Farming Systems, AI in Food Science
30 Module 4 Smart Agriculture Concepts – Part 2 Future Trends in AI-driven Digital Agriculture

Practical Session Plan – Agricultural Informatics & Artificial Intelligence

Practical No. Title / Experiment Activities / Tasks Expected Outcome
P1 Computer System Familiarization Identify parts, boot process Understand computer hardware
P2 Hands-on with OS File management, settings, utilities Operate OS confidently
P3 MS Word Create agri reports, tables Prepare documentation
P4 MS Excel – Data Entry Create spreadsheets Handle agricultural data
P5 MS Excel – Formulas SUM, AVG, IF, VLOOKUP, charts Analyze datasets
P6 MS PowerPoint Scientific slide preparation Develop presentations
P7 Internet Tools Searching, downloading reports Efficient information retrieval
P8 Agriculture Web Platforms mKisan, eNAM, AP agrisites Use ICT platforms
P9 DBMS – Creating Tables Create simple DB for soil/crop Construct basic databases
P10 DBMS – Queries SELECT commands, filters Retrieve agricultural data
P11 Algorithm Activity Flowchart creation Understand programming logic
P12 ICT Tools Use DSS, crop calendars Apply ICT apps
P13 Crop Simulation Tools Try sample model outputs Understand model functioning
P14 Geospatial Tools GPS/RS demo, mapping Interpret spatial data
P15 AI & IoT Demo Sensor reading, simple ML demo Understand smart farming tools

📚 Suggested Readings

S.No. Title Author(s) / Publisher
1 Concepts and Techniques of Programming in C Dhabal Prasad Sethi & Manoranjan, Wiley India
2 Fundamentals of Computer V. Rajaraman
3 Introduction to Information Technology Pearson
4 Introduction to Database Management System C. J. Date
5 Introductory Agri-Informatics Mahapatra, Subrat K. et al., Jain Brothers Publication