Content

Introduction
1. Theoretical foundations of hyperspectral sensing in agriculture
2. Hardware for hyperspectral imaging of agricultural crops
3. Hyperspectral imaging of plant samples
4 Building classification models based on hyperspectral data using machine learning algorithms 4.1 Loading hyperspectral images and training data 4.2 Labeling hyperspectral data and creating a training dataset 4.3 Developing a reference (baseline) model 4.4 Creating a classification model 4.5 Model-based diagnosis of crop diseases using hyperspectral data
5 Spectral diagnostics of crop phytopathologies using trained models