Digital Agriculture & Data-Driven Farming

Week 1: Foundations & Agricultural Data

1. Day 1: Introduction to AI in agriculture — crop yield, disease detection, and weather prediction
2. Day 2: Agricultural fundamentals — soil, crop cycles, precision farming, and irrigation
3. Day 3: Agricultural data types — satellite, drone, IoT sensors, climate, soil, and market data
4. Day 4: Python for agricultural data analysis — NumPy and Pandas
5. Day 5: Data sources, preprocessing, and exploratory analysis of crop-yield datasets

Week 2: Computer Vision for Crops

1. Day 1: Computer vision applications in farming, drones, and agricultural machinery
2. Day 2: Image processing with OpenCV — resizing, cropping, and color spaces
3. Day 3: Plant disease datasets and PlantVillage exploration
4. Day 4: CNNs, image augmentation, and transfer learning with TensorFlow/Keras
5. Day 5: Build and evaluate a leaf disease classification model

Week 3: Crop Forecasting & Machine Learning

1. Day 1: Regression models for crop-yield prediction
2. Day 2: Feature engineering using weather, soil pH, and NDVI
3. Day 3: Time-series forecasting with ARIMA and Prophet
4. Day 4: Decision trees and Random Forest for fertilizer recommendations
5. Day 5: Model evaluation using MAE, RMSE, and R²; hands-on crop-yield prediction project

Week 4: IoT & Remote Sensing

1. Day 1: IoT in agriculture — sensors, edge devices, and data flow
2. Day 2: Remote sensing, satellite bands, NDVI, and Google Earth Engine
3. Day 3: Object detection using YOLO for fruit counting and pest identification
4. Day 4: Agricultural data integration and precision-farming applications
5. Day 5: Practical project — analyze satellite or sensor data for an agricultural use case

Week 5: Advanced AI Applications & Deployment

1. Day 1: NLP for farmers — crop-advisory chatbot development
2. Day 2: Reinforcement learning concepts for automated irrigation
3. Day 3: Machine learning for agricultural market-price forecasting
4. Day 4: Streamlit application development and agricultural dashboard design
5. Day 5: Data privacy, AI ethics, model limitations, and application testing

Week 6: Capstone Projects & Final Assessment

1. Day 1: Develop a crop disease detection web application
2. Day 2: Build a crop-yield prediction model and dashboard
3. Day 3: Integrate data visualization and model predictions
4. Day 4: Test models, evaluate results, and prepare project documentation
5. Day 5: Final project demonstration, feedback, revision, and future learning roadmap