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drone_data's Introduction

Crop Monitoring based on drone data

This repository is made to dispose of several drone-based tools for crop monitoring. Currently, there are available examples for:

  • Drone data reading
  • Spectral indexes calculation
  • Plant level 3D visualization
  • Plant level identification given a trained YOLO model
  • Cluster classification

Multitemporal analysis

Considering that crop monitoring involves a continuous capture of data through its cycle. We have implemented a framework in which the data is stored as a multi-dimensional object. Where besides the x and y axis data, a time dimension is included. This refers to when the image was taken. Regarding the spectral bands, those are located as a fourth dimensiononal array. Wrapping up, the data is a xarray object with dimensions time, Spectral band, Y, X.

Spectral indexes calculation

You can also calculate different vegetation index layers using the function .calculate_vi. To use this function you will need to indicate two parameters:

  • vi: which is the name of the vegetation index
  • expression: is the equation to calculate the vegetation index, eg. "((green_msgreen_ms) - (red_msred_ms))/((green_msgreen_ms) + (red_msred_ms))" will calculate the modified green red vegetation index.
### Calculando indices vegetales
dronedata.calculate_vi('ndvi')
dronedata.calculate_vi(vi = 'mgrvi', expression = "((green_ms*green_ms) - (red_ms*red_ms))/((green_ms*green_ms) + (red_ms*red_ms))"

the following table contains different VI, which can be obtained from combining RGB and NIR spectral bands:

the plotsingleband() function will a single spectral band

m.plot_singleband('ndvi')

CCAFS

drone_data's People

Contributors

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