Development of a species identification system of Japanese bats from echolocation calls using convolutional neural networks

2021 ◽  
pp. 101253
Author(s):  
Keigo Kobayashi ◽  
Keisuke Masuda ◽  
Chihiro Haga ◽  
Takanori Matsui ◽  
Dai Fukui ◽  
...  
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Convolutional Neural Networks ◽  
Identification System ◽  
Echolocation Calls

Cognitive spectroscopy for wood species identification: near infrared hyperspectral imaging combined with convolutional neural networks

The Analyst ◽  
2019 ◽  
Vol 144 (21) ◽  
pp. 6438-6446
Author(s):  
Hideaki Kanayama ◽  
Te Ma ◽  
Satoru Tsuchikawa ◽  
Tetsuya Inagaki
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Hyperspectral Imaging ◽  
Convolutional Neural Networks ◽  
Wood Species ◽  
Near Infrared ◽  
Wood Properties ◽  
Identification System ◽  
Illegal Logging ◽  
System P

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

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

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.

Hand Gesture Identification System Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Siddhartha Arjaria ◽  
Riya Sahu ◽  
Sejal Agrawal ◽  
Suyash Khare ◽  
Yashi Agarwal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hand Gesture ◽  
Identification System

Image-based species identification of wild bees using convolutional neural networks

2020 ◽  
Vol 55 ◽  
pp. 101017 ◽  
Author(s):  
Keanu Buschbacher ◽  
Dirk Ahrens ◽  
Marianne Espeland ◽  
Volker Steinhage
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Convolutional Neural Networks ◽  
Wild Bees

scholarly journals Fish Recognition Model for Fraud Prevention using Convolutional Neural Networks

2021 ◽  
Author(s):  
Rhayane Monteiro ◽  
Morgana Ribeiro ◽  
Calebi Viana ◽  
Mario Wedney de Lima Moreira ◽  
Glacio Araújo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Fish Species ◽  
Effective Solution ◽  
Food Fraud ◽  
Fraud Prevention ◽  
Recognition Model ◽  
Classification Tasks

Abstract Fraud, misidentification, and adulteration of food, whether unintentional or purposeful, are a worldwide and growing concern. Aquaculture and fisheries are recognized as one of the sectors most vulnerable to food fraud. Besides, a series of risks related to health and distrust between consumer and popular market that this sector develop an effective solution for fraud control. Species identification is an essential aspect to expose commercial fraud. Convolutional neural networks (CNNs) are one of the most powerful tools for image recognition and classification tasks. Thus, the objective of this study is to propose a model of recognition of fish species based on CNNs. The results obtained show an algorithm with an accuracy of 86%, providing an effective solution to prevent fish fraud.

Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry

2019 ◽  
Vol 10 (9) ◽  
pp. 1490-1500 ◽  
Author(s):  
Patrick C. Gray ◽  
Kevin C. Bierlich ◽  
Sydney A. Mantell ◽  
Ari S. Friedlaender ◽  
Jeremy A. Goldbogen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Convolutional Neural Networks ◽  
Cetacean Species
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