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Program
72 Lessons
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Chapter 1.1: Crop Disease Detection in Modern Agriculture
Chapter 1.2: Role of AI in Smart Farming
Chapter 2.1: Basics of Spectral Imaging
Chapter 2.2: Hyperspectral Data Structure
Chapter 2.3: Plant Spectral Signatures
Chapter 3.1: Data Acquisition Methods
Chapter 3.2: Preprocessing Techniques
Chapter 3.3: Exploratory Hyperspectral Analysis
Chapter 4.1: Preparing Hyperspectral Data for AI
Chapter 4.2: Spectral Feature Extraction
Chapter 5.1: AI Workflow for Disease Classification
Chapter 5.2: Classical Machine Learning Models
Chapter 5.3: Model Training and Optimization
Chapter 6.1: Introduction to Deep Learning
Chapter 6.2: CNN-Based Hyperspectral Analysis
Chapter 6.3: ML vs Deep Learning Model Comparison
Chapter 7.1: Model Evaluation Metrics
Chapter 7.2: Explainable AI for Agriculture
Chapter 7.3: Spectral Feature Importance
Chapter 8.1: Disease Severity Estimation
Chapter 8.2: Disease Probability and Risk Mapping