scholarly journals A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns

2018 ◽  
Author(s):  
Ashesh Chattopadhyay ◽  
Pedram Hassanzadeh ◽  
Saba Pasha
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Test Case ◽  
Climate Data ◽  
Weather Patterns ◽  
Spatio Temporal

Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Alexander Knyshov ◽  
Samantha Hoang ◽  
Christiane Weirauch
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Training Image ◽  
Species Level ◽  
Identification Accuracy ◽  
Support Vector ◽  
Test Case ◽  
Learning Approaches ◽  
Lower Accuracy ◽  
Neural Network Classifiers

Abstract Automated insect identification systems have been explored for more than two decades but have only recently started to take advantage of powerful and versatile convolutional neural networks (CNNs). While typical CNN applications still require large training image datasets with hundreds of images per taxon, pretrained CNNs recently have been shown to be highly accurate, while being trained on much smaller datasets. We here evaluate the performance of CNN-based machine learning approaches in identifying three curated species-level dorsal habitus datasets for Miridae, the plant bugs. Miridae are of economic importance, but species-level identifications are challenging and typically rely on information other than dorsal habitus (e.g., host plants, locality, genitalic structures). Each dataset contained 2–6 species and 126–246 images in total, with a mean of only 32 images per species for the most difficult dataset. We find that closely related species of plant bugs can be identified with 80–90% accuracy based on their dorsal habitus alone. The pretrained CNN performed 10–20% better than a taxon expert who had access to the same dorsal habitus images. We find that feature extraction protocols (selection and combination of blocks of CNN layers) impact identification accuracy much more than the classifying mechanism (support vector machine and deep neural network classifiers). While our network has much lower accuracy on photographs of live insects (62%), overall results confirm that a pretrained CNN can be straightforwardly adapted to collection-based images for a new taxonomic group and successfully extract relevant features to classify insect species.

scholarly journals Image recognition in the artificial agriculture officer

2018 ◽  
Vol 7 (3.3) ◽  
pp. 119
Author(s):  
B Lokesh ◽  
Ravoori Charishma ◽  
Natuva Hiranmai
Keyword(s):  
Neural Networks ◽  
Information Retrieval ◽  
Search Engine ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Crop Production ◽  
Android Application ◽  
Weather Patterns ◽  
Crop Diseases ◽  
The Right

Farmers face a multitude of problems nowadays such as lower crop production, tumultuous weather patterns, and crop infections. All of these issues can be solved if they have access to the right information. The current methods of information retrieval, such as search engine lookup and talking to an Agriculture Officer, have multiple defects. A more suitable solution, that we are proposing, is an android application, available at all times, that can give succinct answers to any question a farmer may pose. The application will include an image recognition component that will be able to recognize a variety of crop diseases in the case that the farmer does not know what he is dealing with and is unable to describe it.  Image recognition is the ability of a computer to recognize and distinguish between different objects, and is actually a much harder problem to solve than it seems. We are using Tensorflow, a tool that uses convolutional neural networks, to implement it  

scholarly journals Spatio–Temporal Image Representation of 3D Skeletal Movements for View-Invariant Action Recognition with Deep Convolutional Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1932 ◽  
Author(s):  
Huy Hieu Pham ◽  
Houssam Salmane ◽  
Louahdi Khoudour ◽  
Alain Crouzil ◽  
Pablo Zegers ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Action Recognition ◽  
Large Scale ◽  
Image Representation ◽  
Human Action ◽  
Computational Time ◽  
Deep Convolutional Neural Networks ◽  
Classification Tasks ◽  
Spatio Temporal

Designing motion representations for 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio–temporal patterns of skeletal movements and how to learn their discriminative features for classification tasks. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (Skeleton Posture-Motion Feature), which is a compact image representation built from skeleton poses and their motions. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the SPMF to enhance their local patterns and form an enhanced action map, namely Enhanced-SPMF. For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.

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