AI in Agriculture
Grow. Monitor. Optimize
Course Objective
To introduce learners to the practical applications of Artificial Intelligence in agriculture — including smart farming, crop monitoring, pest prediction, irrigation automation, and market forecasting. No coding or technical background is required.
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Module 1: AI in Modern Farming – An Introduction
- What is AI? How it’s applied in agriculture
- Traditional vs. Smart farming
- Examples: Precision agriculture, drone monitoring, disease prediction
- AI success stories in India & globally
- Activity:
- Watch case studies of AI usage in Punjab, Israel, or Kenya and list 3 benefits.
Module 2: Crop Monitoring and Disease Prediction
- Using AI models to detect crop diseases via images
- Mobile apps and tools (Plantix, Agremo, PEAT, Nuru)
- Sensors and camera data for early intervention
- AI alerts for weather, soil health, or pest invasion
- Hands-On Task:
- Take or upload a crop image to Plantix or Nuru and analyze the diagnosis report.
Module 3: Soil, Water & Irrigation Management
- Smart irrigation systems using AI
- Soil sensors, moisture meters, and automated watering
- Use of AI for fertilizer recommendations
- Demo:
- Explore CropX or FarmLogs dashboard and create a basic irrigation plan.
Module 4: Drones, Robots, and Smart Machinery
- Role of drones for crop scanning and pesticide spraying
- Automated tractors and harvesters
- AI + IoT in large-scale farming
- Activity:
- View videos of drone-spraying in vineyards or rice fields and write observations.
Module 5: Market Forecasting & Supply Chain Optimization
- AI for yield prediction and demand forecasting
- Using AI tools to decide “When to sow?” and “When to sell?”
- Reducing waste and middlemen with AI platforms
- Simulation:
- Use data from an AI farming app (like SatSure or IBM Watson Decision Platform) to predict crop output.
Module 6: AI Tools for Small Farmers
- Low-cost AI mobile apps in local languages
- WhatsApp-based advisory bots (like KrishiBot)
- Vernacular support, image-based chatbots
- Hands-On:
- Use any local farming AI app (e.g., BharatAgri, Kisan AI Bot) and explore its features.
Module 7: Ethical & Environmental Impact of AI in Agriculture
- Sustainability, data privacy, and equitable access
- Pros and cons of AI-based decisions
- Future of farmers with AI
- Discussion:
- Debate: “Can AI replace the experience of a farmer?”
Module 8: Capstone Project – Smart Farming Plan
- Design an AI-powered solution for irrigation planning
- Create a disease detection kit using an AI app
- Build a 5-step smart farming model for a crop (e.g., wheat or tomatoes)
- Make a crop calendar using AI data tools
Deliverables:
- Planning document
- Tool screenshots or demos
- 2-minute video explanation or written report
Course Outcome
By the end of this course, learners will:
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Duration:
- Course Duration: 5–6 weeks
- Format: Online / On-field blended (optional)
- Level: Beginner (No coding)
- Audience: School/college students, agri-startups, farm educators, rural youth