Enterprise Scale AI for Sustainable Agriculture

WEEK 1: AGRICULTURAL DATA & PYTHON

Day 1: Crop science basics: growth stages, yield factors, and sustainable agriculture

Day 2: Agricultural data sources: FAO, ICAR, PlantVillage, and weather datasets

Day 3: Farm IoT overview: sensors, drones, and agricultural data types

Day 4: Python and Pandas for soil, weather, and crop-yield data

Day 5: Time-series and geospatial basics; mini-task: explore an Indian crop-yield dataset

WEEK 2: COMPUTER VISION FOR CROPS

Day 1: OpenCV and digital image basics for plant images

Day 2: Plant disease classification: healthy vs diseased leaves

Day 3: CNNs and transfer learning with ResNet/EfficientNet

Day 4: Data augmentation, model evaluation, and Grad-CAM explainability

Day 5: Train and deploy a PlantVillage leaf-disease classifier using Streamlit

WEEK 3: CROP-YIELD PREDICTION & MACHINE LEARNING

Day 1: Yield-related features: rainfall, soil nutrients, and NDVI

Day 2: Regression models for yield prediction: XGBoost and LightGBM

Day 3: Forecasting crop trends with time-series methods and LSTM concepts

Day 4: Hyperparameter tuning and evaluation: Optuna, MAE, RMSE, and R²

Day 5: SHAP explainability; mini-project: predict district-level wheat yield

WEEK 4: REMOTE SENSING & GOOGLE EARTH ENGINE

Day 1: Satellite data: Sentinel-2, Landsat, and MODIS

Day 2: Google Earth Engine: NDVI, EVI, and land-surface temperature

Day 3: Crop mapping and classification using satellite time-series data

Day 4: Drought monitoring, vegetation-condition indices, and change detection

Day 5: Applications in crop health and pest-risk monitoring; mini-project: monitor one farm with GEE

WEEK 5: OBJECT DETECTION & EDGE AI

Day 1: Object detection with YOLO for pests, weeds, and crop features

Day 2: Image annotation with Roboflow or LabelImg

Day 3: Drone imagery and orthomosaic analysis

Day 4: Edge AI concepts and TensorFlow Lite deployment

Day 5: Fruit counting/ripeness detection; mini-project: prototype a mobile-friendly detection app

WEEK 6: PRECISION AGRICULTURE & IoT

Day 1: Precision agriculture and variable-rate prescription maps

Day 2: Soil-health prediction from sensor and field data

Day 3: Smart irrigation and reinforcement-learning concepts

Day 4: Supply-chain loss, cold-storage, weather-risk, and sustainability metrics

Day 5: Carbon and resource-efficiency indicators; mini-project: IoT farm dashboard using sample/live sensor data

WEEK 7: NLP & FARMER ADVISORY SYSTEMS

Day 1: NLP for agricultural reports, mandi updates, and scheme information

Day 2: Retrieval-augmented generation (RAG) for a local-language farmer chatbot

Day 3: Speech-to-text and voice interfaces for accessible farm advisory

Day 4: Market-price forecasting and news-sentiment analysis

Day 5: Rule-based and ML pest advisory; mini-project: voice-based crop advisory prototype

WEEK 8: ENTERPRISE AI & CAPSTONE

Day 1: MLOps, seasonal model drift, and scalable data processing with Spark

Day 2: Business case and ROI for AI on a large farm; farmer data ownership and ethics

Day 3: Industry case studies and capstone problem selection

Day 4: Build an end-to-end pipeline, deploy a prototype, and prepare a business pitch

Day 5: GitHub portfolio, demo presentation, mock interview, and final pitch