Improved Plant Species Identification Using Convolutional Neural Networks with Transfer Learning and Test Time Augmentation

AbstractThe automatic interpretation of sign languages is a challenging task, as it requires the usage of high-level vision and high-level motion processing systems for providing accurate image perception. In this paper, we use Convolutional Neural Networks (CNNs) and transfer learning to make computers able to interpret signs of the Swedish Sign Language (SSL) hand alphabet. Our model consists of the implementation of a pre-trained InceptionV3 network, and the usage of the mini-batch gradient descent optimization algorithm. We rely on transfer learning during the pre-training of the model and its data. The final accuracy of the model, based on 8 study subjects and 9400 images, is 85%. Our results indicate that the usage of CNNs is a promising approach to interpret sign languages, and transfer learning can be used to achieve high testing accuracy despite using a small training dataset. Furthermore, we describe the implementation details of our model to interpret signs as a user-friendly web application.

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Algorithm Selection Framework for Legalization Using Deep Convolutional Neural Networks and Transfer Learning

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ◽

10.1109/tcad.2021.3079126 ◽

2021 ◽

pp. 1-1

Author(s):

Renan Netto ◽

Sheiny Fabre ◽

Tiago Augusto Fontana ◽

Vinicius Livramento ◽

Laercio L. Pilla ◽

...

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks ◽

Algorithm Selection ◽

Selection Framework

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Grape Leaf Multi-disease Detection with Confidence Value Using Transfer Learning Integrated to Regions with Convolutional Neural Networks

2020 IEEE REGION 10 CONFERENCE (TENCON) ◽

10.1109/tencon50793.2020.9293866 ◽

2020 ◽

Author(s):

Sandy Lauguico ◽

Ronnie Concepcion ◽

Rogelio Ruzcko Tobias ◽

Argel Bandala ◽

Ryan Rhay Vicerra ◽

...

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Disease Detection ◽

Grape Leaf ◽

Confidence Value

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Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection

Scientific Reports ◽

10.1038/s41598-021-92891-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Adam Goodwin ◽

Sanket Padmanabhan ◽

Sanchit Hira ◽

Margaret Glancey ◽

Monet Slinowsky ◽

...

Keyword(s):

Neural Networks ◽

Species Identification ◽

Convolutional Neural Networks ◽

Novel Species ◽

Novelty Detection ◽

Mosquito Species ◽

Image Database ◽

Closed Set ◽

Open Set

AbstractWith over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.

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