Predictive Modelling & Remote Sensing in Agritech

Week 1: AI in Agriculture + Foundations

1. Day 01: Introduction to AI in Agriculture – applications in crop yield, disease detection, and weather prediction
2. Day 02: Agriculture fundamentals – soil, crop cycles, precision farming, and irrigation
3. Day 03: Agricultural data types – satellite, drone, IoT sensors, climate, soil, and market data
4. Day 04: Python for agricultural data analysis – environment setup, NumPy, and Pandas
5. Day 05: Agricultural data sources – OpenWeather, NASA EarthData, FAO, and Kaggle; dataset collection

Week 2: Data Preprocessing + Exploratory Data Analysis

1. Day 06: Python programming – variables, loops, functions, and file handling
2. Day 07: NumPy and Pandas – arrays, DataFrames, filtering, and data manipulation
3. Day 08: Data cleaning and preprocessing for noisy sensor, weather, and agricultural datasets
4. Day 09: Data visualization using Matplotlib and Seaborn
5. Day 10: Hands-on: Load a crop-yield dataset, perform EDA, visualize patterns, and prepare a mini-project outline

Week 3: Predictive Modelling for Agriculture

1. Day 11: Machine learning for tabular agricultural data – regression for crop-yield prediction
2. Day 12: Feature engineering – weather indices, soil pH, NDVI, and agricultural variables
3. Day 13: Time-series forecasting – ARIMA and Prophet for rainfall and temperature prediction
4. Day 14: Decision Trees and Random Forest for fertilizer recommendation
5. Day 15: Model evaluation – MAE, RMSE, and R²; hands-on crop-yield prediction using a real dataset

Week 4: IoT + Remote Sensing

1. Day 16: IoT in agriculture – sensors, edge devices, data collection, and data flow
2. Day 17: Introduction to remote sensing – satellite imagery, spectral bands, and NDVI
3. Day 18: Google Earth Engine – satellite data access and basic analysis
4. Day 19: Remote sensing applications – vegetation monitoring and crop-health assessment
5. Day 20: Hands-on: Calculate and visualize NDVI using satellite imagery

Week 5: Advanced AI Applications in Agriculture

1. Day 21: Object detection using YOLO – fruit counting, pest detection, and crop monitoring
2. Day 22: NLP for farmers – agricultural advisory chatbots and FAQ systems
3. Day 23: Reinforcement learning – automated irrigation and smart farming concepts
4. Day 24: AI-based market prediction – crop price forecasting with external factors
5. Day 25: Ethics and challenges – data privacy, model bias, farmer adoption, and responsible AI

Week 6: NLP + Reinforcement Learning + Deployment

1. Day 26: Building an agricultural FAQ chatbot using RAG
2. Day 27: Speech-to-text for regional languages and voice-based agricultural advisory
3. Day 28: Smart irrigation simulation using Reinforcement Learning and OpenAI Gym
4. Day 29: MLOps fundamentals – MLflow, experiment tracking, and model management
5. Day 30: Model deployment using FastAPI and introduction to Docker

Week 7: Capstone Project 1 – AI-Based Crop Disease Detection

1. Day 31: Project planning, dataset collection, and preprocessing for disease detection
2. Day 32: Model development and training for crop disease classification
3. Day 33: Model integration with a Streamlit web application
4. Day 34: Application testing, performance evaluation, and improvements
5. Day 35: Finalization and deployment of the crop disease detection application

Week 8: Capstone Project 2 – Smart Agriculture Dashboard

1. Day 36: Smart farm dashboard planning and data integration
2. Day 37: Weather, crop-yield, and environmental data visualization
3. Day 38: Alerts and predictive insights for smart farming
4. Day 39: Dashboard integration, testing, and documentation
5. Day 40: GitHub README, project report, and demonstration video

Week 9: Portfolio + Career Preparation

1. Day 41: Agricultural AI case studies and real-world business applications
2. Day 42: Predictive modelling and remote sensing project revision
3. Day 43: Resume and GitHub portfolio development for AgriTech roles
4. Day 44: Mock interview – machine learning, remote sensing, IoT, and agricultural case studies
5. Day 45: Final project presentation, feedback, and 90-day learning roadmap

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