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Deep Plant Phenomics is no longer actively maintained. It is available here for historical purposes - however, it is provided as-is with no updates or bug fixes planned.

See this thread for discussion.

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. This means that it is able to run on both CPUs and GPUs, and scale easily across devices.

Read the doumentation for tutorials, or see the included examples. You can also read the paper.

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

What's Deep Learning?

Principally, DPP provides deep learning functionality for plant phenotyping and related applications. Deep learning is a category of techniques which encompasses many different types of neural networks. Deep learning techniques lead the state of the art in many image-based tasks, including image classification, object detection and localization, image segmentation, and others.

What Can I Do With This?

This package provides two things:

1. Useful tools made possible using pre-trained neural networks

For example, calling tools.predict_rosette_leaf_count(my_files) will use a pre-trained convolutional neural network to estimate the number of leaves on each rosette plant.

2. An easy way to train your own models

For example, using a few lines of code you can easily use your data to train a convolutional neural network to rate plants for biotic stress. See the tutorial for how the leaf counting model was built.


Example Usage

Train a simple regression model:

import deepplantphenomics as dpp

model = dpp.RegressionModel(debug=True)

# 3 channels for colour, 1 channel for greyscale
channels = 3

# Setup and hyperparameters
model.set_image_dimensions(256, 256, channels)

# Load dataset of images and ground-truth labels

# Use a predefined model

# Train!


  1. git clone
  2. pip install ./deepplantphenomics

Note: The package now requires Python 3.6 or greater. Python 2.7 is no longer supported.