scholarly journals Fine-Grained Wood Species Identification Using Convolutional Neural Networks

2019 ◽  
pp. 67-77 ◽  
Author(s):  
Dmitrii Shustrov ◽  
Tuomas Eerola ◽  
Lasse Lensu ◽  
Heikki Kälviäinen ◽  
Heikki Haario
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Convolutional Neural Networks ◽  
Wood Species ◽  
Fine Grained

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.

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

Multi-feature fusion of convolutional neural networks for Fine-Grained ship classification

2019 ◽  
Vol 37 (1) ◽  
pp. 125-135 ◽  
Author(s):  
Sizhe Huang ◽  
Huosheng Xu ◽  
Xuezhi Xia ◽  
Fan Yang ◽  
Fuhao Zou
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Fine Grained ◽  
Ship Classification

scholarly journals Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Teja Kattenborn ◽  
Jana Eichel ◽  
Fabian Ewald Fassnacht
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Low Cost ◽  
Training Data ◽  
Observation Data ◽  
Visual Interpretation ◽  
Fine Grained ◽  
Wide Range ◽  
Vegetation Species

AbstractRecent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.

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.

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