Overview

This output represents the intelligence core of our agricultural disaster assessment system. Using advanced machine learning and computer vision techniques, we're developing AI models that can automatically classify and quantify agricultural damage from satellite imagery and UAV data.

The AI system processes multi-spectral imagery, flood maps, and digital surface models to produce detailed damage assessments for different crop types, infrastructure, and land use categories.

AI Processing Pipeline

Our end-to-end pipeline integrates data from Outputs 1 & 2, applying state-of-the-art deep learning models for semantic segmentation, object detection, and change detection to produce actionable damage classifications.

Machine Learning Models

Crop Damage Classifier

94.2%
Training Complete

Deep CNN for pixel-level crop damage classification across paddy rice, wheat, and sugarcane systems.

Infrastructure Detector

89.7%
Validation Phase

YOLO-based model for detecting damaged buildings, roads, and agricultural infrastructure.

Change Detection Model

91.5%
Testing

Siamese network for temporal change analysis between pre and post-disaster imagery.

Flood Severity Estimator

87.3%
In Development

Regression model for estimating flood depth and duration impact on agricultural areas.

Development Progress

Data Preprocessing Pipeline
100%
Model Architecture Development
95%
Training & Validation
85%
Performance Optimization
70%
Integration Testing
60%

Damage Classification Schema

Our standardized classification system enables consistent damage assessment across different crop types and geographic regions.

No Damage
Normal crop condition, no visible impact
Light Damage
0-25% crop loss, recoverable
Moderate Damage
25-50% crop loss, significant impact
Severe Damage
50-75% crop loss, major impact
Total Loss
75-100% crop loss, complete destruction
Infrastructure
Buildings, roads, irrigation systems
AI Model Prediction Visualization Interface

Technical Implementation

Deep Learning Framework

  • Primary Framework: PyTorch with torchvision for computer vision tasks
  • Model Architectures: U-Net for segmentation, ResNet backbone for classification
  • Training Infrastructure: Multi-GPU setup with distributed training capabilities
  • Data Augmentation: Geometric and radiometric transforms for robustness

Feature Engineering

  • Multi-spectral band combinations (NDVI, NDWI, SAVI)
  • Texture analysis using Local Binary Patterns and GLCM
  • Elevation derivatives from DSM data
  • Temporal features from multi-date imagery

Quality Assurance

  • Cross-validation with stratified sampling
  • Ground truth verification using field survey data
  • Confusion matrix analysis and class-balanced metrics
  • Uncertainty quantification for prediction confidence