Insect Species Identification System Based on Deep Learning

Author(s):  
Yung-Kuan Chan ◽  
Wei-Chin Lee ◽  
Wen-Xuan Chen ◽  
Yu-Chun Chen ◽  
Wu-Chun Tu ◽  
...  
2021 ◽  
Vol 9 ◽  
Author(s):  
Jianqiang Sun ◽  
Ryo Futahashi ◽  
Takehiko Yamanaka

Citizen science is essential for nationwide ecological surveys of species distribution. While the accuracy of the information collected by beginner participants is not guaranteed, it is important to develop an automated system to assist species identification. Deep learning techniques for image recognition have been successfully applied in many fields and may contribute to species identification. However, deep learning techniques have not been utilized in ecological surveys of citizen science, because they require the collection of a large number of images, which is time-consuming and labor-intensive. To counter these issues, we propose a simple and effective strategy to construct species identification systems using fewer images. As an example, we collected 4,571 images of 204 species of Japanese dragonflies and damselflies from open-access websites (i.e., web scraping) and scanned 4,005 images from books and specimens for species identification. In addition, we obtained field occurrence records (i.e., range of distribution) of all species of dragonflies and damselflies from the National Biodiversity Center, Japan. Using the images and records, we developed a species identification system for Japanese dragonflies and damselflies. We validated that the accuracy of the species identification system was improved by combining web-scraped and scanned images; the top-1 accuracy of the system was 0.324 when trained using only web-scraped images, whereas it improved to 0.546 when trained using both web-scraped and scanned images. In addition, the combination of images and field occurrence records further improved the top-1 accuracy to 0.668. The values of top-3 accuracy under the three conditions were 0.565, 0.768, and 0.873, respectively. Thus, combining images with field occurrence records markedly improved the accuracy of the species identification system. The strategy of species identification proposed in this study can be applied to any group of organisms. Furthermore, it has the potential to strike a balance between continuously recruiting beginner participants and updating the data accuracy of citizen science.


2019 ◽  
Vol 76 (4) ◽  
pp. 1222-1227
Author(s):  
Chia‐Hung Hsieh ◽  
Chin‐Gi Huang ◽  
Wen‐Jer Wu ◽  
Hurng‐Yi Wang

Author(s):  
Romain Thevenoux ◽  
Van Linh LE ◽  
Heloïse Villessèche ◽  
Alain Buisson ◽  
Marie Beurton-Aimar ◽  
...  

The Analyst ◽  
2019 ◽  
Vol 144 (21) ◽  
pp. 6438-6446
Author(s):  
Hideaki Kanayama ◽  
Te Ma ◽  
Satoru Tsuchikawa ◽  
Tetsuya Inagaki

From the viewpoint of combating illegal logging and examining wood properties, there is a contemporary demand for a wood species identification system.


Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5523 ◽  
Author(s):  
Nada Alay ◽  
Heyam H. Al-Baity

With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.


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