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Digital Agriculture Laboratory
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Digital Agriculture Laboratory  | College of Agricultural and Environmental Sciences | College of Engineering

Digital Agriculture Laboratory

College of Agricultural and Environmental Sciences | College of Engineering
  • UC Davis

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  • Home
  • RS Data Base
    • 1- Data Base Content
    • 2- Overview
    • 3- Platforms
      • 3.1. Unmanned Aerial Systems (UAS)
      • 3.2.Manned Aircraft
      • 3.3. Satellite
    • 4- Sensors
      • 4.1.  RGB
      • 4.2. Multispectral
      • 4.3. Hyperspectral
      • 4.4. Thermal
      • 4.5. Lidar
      • 4.6. Radar
      • 4.7. Acoustic
    • 5- Applications
      • 5.1. Water Status
      • 5.2. Disease control
      • 5.3. Yield mapping and prediction
      • 5.4. Nutrient management
      • 5.5. Salinity
      • 5.6. Phenotyping
    • 6- Data processing and analysis
      • 6.1. Preprocessing
        • 6.1.1. Radiometric Calibration
        • 6.1.2. Georeferencing
        • 6.1.3. Mosaicing
        • 6.1.4. Point cloud and Digital Surface Models
      • 6.2. Processing
        • 6.2.1. Noise removal and segmentation
        • 6.2.2. Raw Feature Extraction
      • 6.3. Analytics
        • 6.3.1. Multicollinearity
        • 6.3.2. Feature engineering and feature selection
        • 6.3.3.Data mining
      • 6.4. Regression\classification
    • 7- Course of Action
  • Decision Support Tools
    • GDD Calculator
    • Radiative transfer Modeling
    • When to fly?
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Breadcrumb
  1. Digital Agriculture Laboratory
  2. RS Data Base
  3. Remote Sensing Database

1- Table of Contents

2- Overview

3- Platforms

3.1. Unmanned Aerial Systems (UAS)

3.2.Manned Aircraft

3.3. Satellite

4- Sensors

4.1.  RGB

4.2. Multispectral

4.3. Hyperspectral

4.4. Thermal

4.5. Lidar

4.6. Radar

4.7. Acoustic

5- Applications

5.1. Water Status

5.2. Disease control

5.3. Yield mapping and prediction

5.4. Nutrient management

5.5. Salinity

5.6. Phenotyping

6- Data processing and analysis

6.1. Preprocessing

6.1.1. Radiometric Calibration

6.1.2. Georeferencing

6.1.3. Mosaicing

6.1.4. Point cloud and Digital Surface Models

6.2. Processing

6.2.1. Noise removal and segmentation

6.2.2. Raw Feature Extraction

6.3. Analytics

6.3.1. Multicollinearity

6.3.2. Feature engineering and feature selection

6.3.3.Data mining

6.4. Regression\classification

 

 

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