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https://github.com/azad77/detecting-and-counting-plants-using-computer-vision-techniques

https://github.com/azad77/detecting-and-counting-plants-using-computer-vision-techniques

Science Score: 36.0%

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Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: Azad77
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 966 KB
Statistics
  • Stars: 5
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License

README.md

Plant Detection and Counting Using Computer Vision Techniques

Author: Azad Rasul (azad.rasul@soran.edu.iq)
Attribution: The code was inspired by the DroneMapper CropAnalysis project.

Introduction

This project explores the use of computer vision techniques to detect and count plants using raster data. By leveraging tools like OpenCV, rasterio, and EarthPy, the tutorial processes raster datasets to identify and visualize plant locations. The method employs blob detection to count plants within a specified plot area accurately, providing valuable insights for applications in agriculture and environmental monitoring.

Data Download

Download the data from: DroneMapper Crop Analysis Data and extract it into the data/ directory of your notebook.

Dependencies

The following Python libraries are required to run the code in this repository:

  • GeoPandas
  • NumPy
  • Rasterio
  • Matplotlib
  • EarthPy
  • OpenCV
  • Pandas

You can install them using pip:

bash pip install geopandas numpy rasterio matplotlib earthpy opencv-python pandas

Code Overview

  1. Import Libraries The project starts by importing essential libraries for geospatial data handling, raster data processing, computer vision, and data visualization.

  2. Reading and Processing Data readplotdata(filepath): Reads the plot data from a shapefile using GeoPandas. loadraster(filepath): Loads raster data from a file using Rasterio. maskrasterwithplot(raster, plot): Masks the raster data with the plot extent, cropping and filtering the data. generateplantmodel(demclip, dtmclip): Generates a plant model by calculating the difference between the DEM (Digital Elevation Model) and the DTM (Digital Terrain Model).

  3. Visualization plotplantmodelandbinary(plantmodel, binaryimage, plot_extent): Plots the plant model and binary image using EarthPy and Matplotlib.

  4. Plant Detection setupblobdetector(): Configures the SimpleBlobDetector with parameters to detect blobs (plants) in an image. detectplants(binaryimageint, rgb): Detects plant keypoints in the binary image. plotplant_count(rgb, keypoints): Visualizes the detected plants with blue circles on the RGB image.

  5. Image Normalization and Building True Color Image normalizergb(image): Normalizes RGB image data to the range [0, 255]. buildtruecolorimage(rgb_clip): Ensures RGB values are correctly scaled for true color representation. Usage After setting up the environment and downloading the necessary data, run the Python scripts or Jupyter notebooks provided in this repository. Follow the instructions within the code to process the data, detect plants, and visualize the results.

License

This project is licensed under the MIT License.

Acknowledgements

This project is heavily inspired by the DroneMapper CropAnalysis project. Special thanks to the original authors for providing the data and initial code structure.

Owner

  • Name: Dr Azad Rasul
  • Login: Azad77
  • Kind: user
  • Company: Soran University

As a geographer, I use remote sensing and GIS methods and techniques to study LST, urban environment, earth observation and natural disasters.

GitHub Events

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Committers

Last synced: 12 months ago

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  • Total Commits: 4
  • Total Committers: 1
  • Avg Commits per committer: 4.0
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  • Committers: 1
  • Avg Commits per committer: 4.0
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Dr Azad Rasul a****l@s****q 4
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soran.edu.iq: 1

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