LeafsnapNet: An Experimentally Evolved Deep Learning Model for Recognition of Plant Species based on Leafsnap Image Dataset

Ajayi, Oluwaseun T.; Kala, Jules Raymond; Ajala, Sunday; Abayomi, Abdultaofeek; Badejo, Joke A.; Adetiba, Emmanuel

doi:10.3844/jcssp.2021.349.363

Cited by 10 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AlexNet [50], VggNet [52], and MobileNet [53,54] are the pretrained CNN models examined in this research. With a 1000 weight SoftMax, AlexNet has five convolutional layers, three max pooling layers, dropout, and three fully connected layers.…”

Section: Convolutional Neural Network For Deep Learningmentioning

confidence: 99%

Deep-Learning Based Estimation of Dielectrophoretic Force

2021

Self Cite

View full text Add to dashboard Cite

The ability to accurately quantify dielectrophoretic (DEP) force is critical in the development of high-efficiency microfluidic systems. This is the first reported work that combines a textile electrode-based DEP sensing system with deep learning in order to estimate the DEP forces invoked on microparticles. We demonstrate how our deep learning model can process micrographs of pearl chains of polystyrene (PS) microbeads to estimate the DEP forces experienced. Numerous images obtained from our experiments at varying input voltages were preprocessed and used to train three deep convolutional neural networks, namely AlexNet, MobileNetV2, and VGG19. The performances of all the models was tested for their validation accuracies. Models were also tested with adversarial images to evaluate performance in terms of classification accuracy and resilience as a result of noise, image blur, and contrast changes. The results indicated that our method is robust under unfavorable real-world settings, demonstrating that it can be used for the direct estimation of dielectrophoretic force in point-of-care settings.

show abstract

Section: Convolutional Neural Network For Deep Learningmentioning

confidence: 99%

Deep-Learning Based Estimation of Dielectrophoretic Force

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…We have used four pre-trained CNN architectures viz . AlexNet 67 , MobileNetV2 68 , 69 , GoogLeNet 70 and ResNet-50 71 as the base architectures for deep regression analysis. Table 2 presents a brief overview of these pre-trained CNN architectures.…”

Section: Methodsmentioning

confidence: 99%

“…Notable hyperparameters such as the learning rate and batch size were appropriately tuned to minimize the cost function and speedup optimization while ensuring the models converge to the global minimum, thereby solving the problem of overfitting 69 , 72 . Table 3 presents the CNN hyperparameters used.…”

Section: Methodsmentioning

confidence: 99%

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force

Ajala

Jalajamony

Nair

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework was validated using experimental results. Images of pearl chain alignment at varying input voltages were used to build deep regression models using modified ML and CNN architectures that can correlate pearl chain alignment patterns of Saccharomyces cerevisiae(yeast) cells and polystyrene microbeads to DEP force. Various ML models such as K-Nearest Neighbor, Support Vector Machine, Random Forest, Neural Networks, and Linear Regression along with DL models such as Convolutional Neural Network (CNN) architectures of AlexNet, ResNet-50, MobileNetV2, and GoogLeNet have been analyzed in order to build an effective regression framework to estimate the force induced on yeast cells and microbeads. The efficiencies of the models were evaluated using Mean Absolute Error, Mean Absolute Relative, Mean Squared Error, R-squared, and Root Mean Square Error (RMSE) as evaluation metrics. ResNet-50 with RMSPROP gave the best performance, with a validation RMSE of 0.0918 on yeast cells while AlexNet with ADAM optimizer gave the best performance, with a validation RMSE of 0.1745 on microbeads. This provides a baseline for further studies in the application of deep learning in DEP aided Lab-on-Chip devices.

show abstract

“…HoG was used in classifying the features and CNN for identification purposes. Adetiba et al (2021) leveraged on five pre-trained CNN models (Alex Net, Goog LeNet, VGG-19, ResNet50 and MobileNetV2) and Leaf snap image dataset of 185 plant species to empirically develop an accurate plant species recognition. Among the pre-trained models, MobileNetV2 with ADAM optimizer gave the highest testing accuracy of 92.33%.…”

Section: Related Workmentioning

confidence: 99%

Medicinal Plant Identification using Gabor Filters and Deep Learning Techniques: A Paper Review

Oppong¹,

Twum²,

Hayfron-Acquah³

et al. 2021

Journal of Computer Science

View full text Add to dashboard Cite

Computer-aided identification of plants is a branch of machine learning that has become more recognized recently and proves itself as a vital tool in numerous sectors including pharmacological science, forestry and agriculture. This has essentially generated a zeal in creating automated systems for the identification of diverse species of plants. This study reviewed plant species classification relying on leaf textural features using Gabor filters and revealed that Gabor filters perform better when combined with other feature extraction methods. Therefore, this study proposes using Log-Gabor filter in the field of plant identification to improve accuracy since they overcome the drawbacks of Gabor filters which are; the maximum bandwidth of a Gabor filter is limited to approximately one octave and Gabor filters are not optimal if one is seeking broad spectral information with maximal spatial localization.

show abstract

LeafsnapNet: An Experimentally Evolved Deep Learning Model for Recognition of Plant Species based on Leafsnap Image Dataset

Cited by 10 publications

References 21 publications

Deep-Learning Based Estimation of Dielectrophoretic Force

Deep-Learning Based Estimation of Dielectrophoretic Force

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force

Medicinal Plant Identification using Gabor Filters and Deep Learning Techniques: A Paper Review

Contact Info

Product

Resources

About