Hardware for Multispectral Capture

Have more questions? Submit a request

Getting Started with Multispectral Sensors for Plant Health Analysis in DroneDeploy

DroneDeploy enables users to process multispectral imagery and extract valuable insights into plant health. By utilizing multispectral sensors, you can monitor crop conditions, detect early signs of stress, and optimize agricultural practices.

This guide will walk you through the basics of starting with multispectral sensors on DroneDeploy, highlighting supported equipment and sensor details, and explaining how to analyze plant health data effectively.

Supported Multispectral Sensors and Equipment

To leverage multispectral processing capabilities in DroneDeploy, you need compatible sensors mounted on supported drones. These sensors must be set up to capture imagery during automated flights controlled via DroneDeploy. After the flight, the data can be uploaded for processing, and you can toggle the "3rd party camera" option in DroneDeploy preferences to control the camera settings.

Compatible Multispectral Sensors for Processing

Here’s a breakdown of the multispectral sensors supported by DroneDeploy, along with the corresponding flight setup and upload sections.

Camera Type Flight Setup Where to Upload
Mavic 3 Multispectral No, use the DJI Pilot 2 app or another flight app 4-band processing support 
Phantom 4 Pro Multispectral No, use DJI GS Pro or another flight app Multispectral Upload Section 
Sentera High-Precision NDVI Single Sensor & Sentera Double 4K Yes, toggle 3rd party camera within preferences Upload like a normal map 
AgEagle Micasense RedEdge-M, MX Yes, toggle 3rd party camera within preferences Multispectral Upload Section 
Parrot Sequoia Not supported Not supported
Altum Yes, toggle 3rd party camera within preferences Multispectral Upload Section 


Plant Health Analysis with Multispectral Data

Once you’ve successfully uploaded your multispectral imagery, you can use DroneDeploy’s Plant Health Layer to analyze the data and evaluate the condition of the plants. The platform allows you to toggle between filters and algorithms to gain insights into plant health indicators.

Understanding Plant Health Layers and Band Orders

Different multispectral sensors capture varying wavelengths of light, which are critical for assessing plant health. Below are the band orders available depending on the sensor:

  • RGB (Red, Green, Blue) – Standard visible light, useful for general imagery.
  • NRG (Near Infrared, Red, Green) – Enhances the ability to detect plant health with better sensitivity to chlorophyll.
  • NGB (Near Infrared, Green, Blue) – Similar to NRG but optimized for specific crops and conditions.
  • RGN (Red, Green, Near Infrared) – A standard band setup for general crop monitoring.
  • NB (Near Infrared, Blue) – Provides insights into water stress and other plant factors.
  • NG (Near Infrared, Green) – Effective for distinguishing between healthy and stressed vegetation.

The Mavic 3 Multispectral captures four bands (Red, Green, RE, and NIR), excluding Blue wavelengths, which impacts how the imagery is processed. When using this sensor, you must upload the four bands for accurate orthophoto creation and plant health analysis.

Processing and Viewing Plant Health Data

After uploading your multispectral imagery, you can toggle the "Plant Health Layer" within DroneDeploy to assess your crops' health. Depending on the sensor, you can apply various algorithms to analyze plant conditions.

  • Algorithms for RGB Cameras (Red, Green, Blue):

    • VARI (Vegetation Atmospherically Resistant Index): Best for detecting general plant health in visible light imagery.
  • Algorithms for Near Infrared (NIR) Cameras:

    • NDVI (Normalized Difference Vegetation Index): The most widely used index for assessing vegetation health.
    • ENDVI (Enhanced NDVI): A better variation in certain soil and vegetation conditions.
    • GNDVI (Green NDVI): An index focused on chlorophyll and stress detection.
    • SAVI (Soil Adjusted Vegetation Index): Ideal for areas with low vegetation or mixed backgrounds.
    • OSAVI (Optimized Soil Adjusted Vegetation Index): Similar to SAVI, it is further refined to minimize soil influence.
    • RDVI (Renormalized Difference Vegetation Index): Provides a more apparent distinction between plant health and background variations.

To get started, you can select the appropriate filter and algorithm based on the sensor you used and the plant health aspect you want to monitor.

Legacy Uploaders and Band Verification

Multispectral uploads are currently only supported via the Legacy Uploader, so use that option for your uploads. It’s also essential to verify the band order used by the sensor manufacturer to ensure correct processing. Each sensor has its band configuration, and DroneDeploy can help you identify which algorithm will work best based on the bands captured.

For example:

  • The Mavic 3 Multispectral captures red, green, RE, and NIR bands.
  • The Phantom 4 Pro Multispectral includes a more comprehensive set of bands, including Blue and NIR, enabling a broader range of analysis.

Supported Sensors: 

  • Mavic 3 Multispectral
  • Phantom 4 Pro Multispectral
  • Sentera High-Precision NDVI Sensors
  • AgEagle Micasense RedEdge-M

Conclusion

Multispectral sensors provide powerful tools for monitoring and assessing plant health, whether you're working with crops, forests, or other vegetation. By using DroneDeploy, you can easily upload and process multispectral imagery to analyze plant conditions and make informed decisions for agricultural management. Remember to verify your sensor’s band order and apply the correct algorithm to get the most accurate insights into plant health.

 

Additional links to Plant Health and Multispectral related articles:

Plant Health

Zone Management

Understanding Vegetation Indices

Filter and Algorithm Types based on Camera Type

Multispectral Imagery for Plant Health

Displaying SHP Plant Health Data in ArcGIS

 

 

Articles in this section

Was this article helpful?
0 out of 1 found this helpful