On Using Transfer Learning For Plant Disease Detection

Code for the paper On Using Transfer Learning For Plant Disease Detection.

https://abhinavsagar.github.io/files/plant_cnn.pdf

Citing

If you find this code useful in your research, please consider citing the paper:

BibTex:

@article{sagar2020using,
  title={On Using Transfer Learning For Plant Disease Detection},
  author={Sagar, Abhinav and Dheeba, J},
  journal={bioRxiv},
  year={2020},
  publisher={Cold Spring Harbor Laboratory}
}

APA:

Sagar, A., & Dheeba, J. (2020). On Using Transfer Learning For Plant Disease Detection. bioRxiv.

Abstract

Deep neural networks has been highly successful in image classification problems. In this paper, we show how neural networks can be used for plant disease recognition in the context of image classification. We have used publicly available Plant Village dataset which has 38 classes of diseases. Hence, the problem that we have addressed is a multi class classification problem. We compared five different architectures including VGG16, ResNet50, InceptionV3, InceptionResNet and DenseNet169 as the backbones for our work. We found that ResNet50 achieves the best result on the test set. For evaluation, we used metrics: accuracy, precision, recall, F1 score and class wise confusion metric. Our model achieves the best of results using ResNet50 with accuracy of 0.982, precision of 0.94, recall of 0.94 and F1 score of 0.94.

Data

The dataset can be downloaded from here.

Environment and tools

1. Jupyter Notebook
2. Numpy
3. Pandas
4. Scikit-image
5. Matplotlib
6. Scikit-learn
7. Keras

Installation

pip install numpy pandas scikit-image matplotlib scikit-learn keras

jupyter notebook

Results

Visualization of Feature Maps

Loss vs Epoch

Accuracy vs Epoch

Confusion Matrix

Comparison of pre trained weights on results

License

MIT License

Copyright (c) 2020 Abhinav Sagar

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