DPP – Rosette Leaf Counter

by Open Source in on December 5, 2017

The DPP paper is now available online!

Deep Plant Phenomics

Deep Plant Phenomics (DPP) is a platform for plant phenotyping using deep learning. Think of it as Keras for plant scientists.

DPP integrates Tensorflow for learning and PlantCV for image processing. This means that it is able to run on both CPUs and GPUs, and scale easily across devices.

DPP is maintained at the Plant Phenotyping and Imaging Research Center (P2IRC) at the University of Saskatchewan. ???

Included models

This package provides a pre-trained model for estimating the leaf count for the rosette plant.

Features

  • Several pre-made networks for common plant phenotyping tasks.
  • Automatic batching and input pipeline.
    • Loaders for some popular plant phenotyping datasets.
    • Plenty of different loaders for your own data, however it exists.
    • Predict classes, values, bounding boxes, or segmentations.
  • Support for semantic segmentation with fully convolutional networks.
  • Tensorboard integration.
  • Easy-to-use API for building new models.
  • Easy to deploy your own models as a Python function!

Installation

Like all packages on AlgoHub, Docker is the simplest way to get started with this model. We have created a pre-built docker image that includes all dependencies needed to run the DPP library.

  • First download the package that contains the pre-trained models. (this item)
  • Extract the .zip to a location of your choosing
  • Be sure you have docker installed!

Run the model

  • Run the docker command below from the location you extracted the package to.

    docker run --mount type=bind,source=$(pwd)/deepplantphenomics/network_states,target=/opt/conda/lib/python2.7/site-packages/deepplantphenomics/network_states --mount type=bind,source=$(pwd)/,target=/data --entrypoint python algohub/dpp /data/examples/rosette_leaf_count_test.py

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Details

  • Released
    :

    December 5, 2017

  • Last Updated
    :

    June 1, 2020

  • Categories
    :
Mike Cowgill at 10:19 pm

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