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Regression in Convolutional Neural Network applied to Plant Leaf Count

Last updated Jun 19, 2026
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README

Regression in Convolutional Neural Network Applied to Plant Leaf Count

[Silva and Gonçalvez, 2019][1]

Keras TensorFlow

Table of Contents

Dataset

Example of the dataset used for these experiments. For more details about the dataset you can visite the CVPP web site.

This repository contains some of experiments used for evaluating the regression with cnn (Convolutional Neural Networks) for counting better leafs. The dataset was available of the contents Computer Vision Problems in Plant Phenotyping.

As result one paper was produced and publicated in the WorkShop of Computer Vision (2019) where you can ready in the following link(da Silva and Gonçalvez (2019)).

Note: this paper was writer 80% in Portuguese, so is crucial you understand this language.

Experiments

Bellow is a description about the experiments:
    • Python script and Jupyter notebook;
    • Was used a desktop computer with Intel(R) Xeon(R) CPU E3-1270@3.80 GHz, 64 GB memory, and NVIDIA Titan V graphics card (5120 Compute Unified Device Architecture - CUDA cores and 12 GB graphics memory). The methods were implemented using Keras-Tensorflow on the Ubuntu 18.04 operating system.

    Getting Started

    Install the following packages

    * Sklearn 0.22.2.

    $ pip install -U scikit-learn
    * TensorFlow
    $ pip install tensorflow
    * Keras
    $ pip install keras
    * Matplotlib
    $ python -m pip install -U matplotlib
    * Os
    $ pip install os-sys
    * Skimage
    $ pip install scikit-image

    Run

    For run the experiment you need to access the respective architecture folder first, you can choose bellow the best way for run.

    If you need run jupyter:

    jupyter-notebook name_experiment.ipynb

    You can run using python script also:

    python name_experiment.py

    Some Results

    Bellow, follow two results that have been applied to the test set using the architecture Xception, and the metrics used was MAE (Mean Absolute error) and R2 (Coefficient of Determination):

    | Architecture | Mean Squared Error| Mean Absolute Error | Coefficient of Determination| |:-------------:|:-------------:|:-------------:|:-------------:| |Xception | 1.09 | 0.46 | 0.96|

    Acknowledgement

    Thanks for the teacher PhD Wesley Nunes Gonçalvez for contributing for this project.

    License

    License

  • MIT license

References

[1]:da Silva, Neemias Bucéli, and Wesley Nunes Gonçalves. "Regression in Convolutional Neural Networks applied to Plant Leaf Counting." Anais do XV Workshop de Visão Computacional. SBC, 2019.

#

Sincerely Neemias B. Silva.

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