Plant identification using deep neural networks via optimization of transfer learning parameters

By Mostafa Mehdipour-Ghazi, Berrin A. Yanikoglu, E. Aptoula. Neurocomputing, 2017

487 citations recorded by the source corpus.

Subfields: Ecology, Media Technology, Plant Science

View Paper: https://www.semanticscholar.org/paper/5bc9c75fd8a5060120e1bbd622cac8322cc623be

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Marcin Wozniak
2. John E. Ball
3. Derek T. Anderson
4. Geeta Rani
5. Sangeeta Vaibhav Meena
6. Jianxin Wang
7. Yu Sun
8. Guan Wang

In Media Technology

1. John E. Ball
2. Derek T. Anderson
3. Jianxin Wang
4. Yu Sun
5. Guan Wang
6. S. Mirjalili
7. Nour Eldeen M. Khalifa
8. Chee Seng Chan

In Plant Science

1. Marcin Wozniak
2. John E. Ball
3. Derek T. Anderson
4. Geeta Rani
5. Sangeeta Vaibhav Meena
6. Jianxin Wang
7. Yu Sun
8. Guan Wang

Citations in the corpus

• A Decade Survey of Transfer Learning (2010–2020) (2020, background): “Firstly, there are two main trends of signal processing, namely image processing [63], [88]–[91], audio analysis [92]–[94].”
• Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach (2021, methodology): “To improve the accuracy of the model, Ghazi et al. used a transfer-learning-based approach on pretrained deep-learning models [40].”
• Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation (2019, unknown): “All the hyperparameters selected here are following the experiences, which also be used in open published literature [13, 30].”
• A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “[63] applied three deep learning architectures, namely, GoogLeNet, AlexNet, and VGGNet, on the LifeCLEF dataset for plant identification.”
• Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community (2017, methodology): “Ghazi et al.(255) used DL to identify plants in photographs using transfer parameter optimization.”
• A comprehensive survey of recent trends in deep learning for digital images augmentation (2021, background): “In (Mehdipour Ghazi et al. 2017), The authors proposes a novel approach for plant identification utilizing deep neural networks adjusted using optimization techniques.”
• Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning (2017, methodology): “The result is consistent with that of [26, 28], where the VGGNet showed better performance in the PlantCLEF plant identification task.”