Program Structure

Module 1: Precision Farming — The New Basics

• What precision farming means in practical terms (beyond “technology”).
• How AI supports farmer decisions: monitoring, prediction, and optimization.
• Key farm challenges: yield variability, input cost, water stress, climate risk.
• Real-world adoption: smallholder vs large farms (constraints and solutions).

Module 2: Farm Data Ecosystem (What Data Matters Most)

• Soil data: texture, pH, EC, NPK, organic carbon—what to collect and why.
• Weather data: rainfall, temperature, humidity, wind; microclimate thinking.
• Crop growth data: sowing dates, phenology, plant density, yield history.
• IoT and field sensors: moisture sensors, weather stations, leaf wetness sensors.
• Data quality reality: missing values, noisy sensors, and field-level variability.

Module 3: Remote Sensing for Crop Health (Satellite + Drone Concepts)

• How satellite/drone imagery helps: monitoring large areas quickly.
• Vegetation indices (NDVI, NDRE concepts): what they indicate and limitations.
• Detecting crop stress: water stress, nutrient stress, pest/disease signatures (conceptual).
• Field zoning: identifying high/medium/low productivity zones.

Module 4: AI for Yield Prediction & Crop Growth Forecasting

• What yield prediction models need: weather + soil + management + imagery.
• Features that matter: GDD, rainfall distribution, soil moisture, crop stage signals.
• Model outputs: yield estimates, confidence levels, risk flags.
• Using predictions to plan inputs and harvest logistics.

Module 5: Smart Irrigation & Water Management

• Water requirement basics: evapotranspiration concepts and crop stages.
• Soil moisture-based irrigation scheduling (rule-based + AI-assisted).
• Detecting irrigation inefficiency and leakage patterns (conceptual).
• Weather-aware irrigation decisions: rainfall forecasting integration.

Module 6: Nutrient Management & Precision Fertilization

• Understanding nutrient deficiency patterns and symptoms.
• Data-driven fertilizer planning: timing, dose, and zone-based application.
• AI-assisted recommendations with constraints (cost, soil health, regulations).
• Reducing overuse: protecting soil health and minimizing runoff.

Module 7: Pest & Disease Detection and Forecasting

• Early warning approach: scouting + weather + crop stage + history.
• Image-based detection basics (leaf disease recognition workflow).
• Outbreak forecasting: humidity/temperature triggers and risk scoring.
• Decision support: when to intervene, and how to reduce chemical dependence.

Module 8: Farm Decision Support Systems (DSS) & Advisory Design

• Turning models into recommendations farmers can act on.
• Building advisory outputs: what to say, when to say it, and how to communicate.
• Alerts and thresholds: designing practical notifications.
• Dashboards and field reports: mapping plots, trends, and action lists.

Module 9: Climate-Smart Farming & Risk Management

• Climate risks: heat stress, drought, excess rainfall, pest shifts.
• Using AI for seasonal planning: crop choice, sowing window, input strategy.
• Resilience practices: mulching, intercropping, soil organic matter, water harvesting.
• Forecast-driven decision-making and contingency planning.

Final Project

• Create an AI-Enabled Smart Crop Management Plan for one crop and one region.
• Include: data plan, monitoring approach, irrigation + nutrient strategy, pest/disease risk workflow, and KPIs.
• Example projects: smart irrigation plan for paddy, disease forecasting for tomato, yield prediction plan for maize, NDVI-based zoning for cotton.