scholarly journals Automatic recognition of strawberry diseases and pests using convolutional neural network

2021 ◽  
Vol 1 ◽  
pp. 100009
Author(s):  
Cheng Dong ◽  
Zhiwang Zhang ◽  
Jun Yue ◽  
Li Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Recognition

scholarly journals Black Sigatoka Classification Using Convolutional Neural Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 323-326
Author(s):  
Cristian A. Escudero ◽  
◽  
Andrés F. Calvo ◽  
Arley Bejarano
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Tree ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Automatic Recognition ◽  
Black Sigatoka ◽  
Atypical Values ◽  
Leaf Image ◽  
Banana Crops

In this paper we present a methodology for the automatic recognition of black Sigatoka in commercial banana crops. This method uses a LeNet convolutional neural network to detect the progress of infection by the disease in different regions of a leaf image; using this information, we trained a decision tree in order to classify the level of infection severity. The methodology was validated with an annotated database, which was built in the process of this work and which can be compared with other state-of-the-art alternatives. The results show that the method is robust against atypical values and photometric variations.

scholarly journals Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0189974 ◽  
Author(s):  
Muthu Subash Kavitha ◽  
Takio Kurita ◽  
Soon-Yong Park ◽  
Sung-Il Chien ◽  
Jae-Sung Bae ◽  
...  
Keyword(s):  
Neural Network ◽  
Stem Cells ◽  
Convolutional Neural Network ◽  
Induced Pluripotent Stem Cells ◽  
Pluripotent Stem Cells ◽  
Automatic Recognition ◽  
Network Approach ◽  
Neural Network Approach ◽  
Induced Pluripotent

scholarly journals Experimenting the Automatic Recognition of Non-Conventionalized Units in Sign Language

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 310
Author(s):  
Valentin Belissen ◽  
Annelies Braffort ◽  
Michèle Gouiffès
Keyword(s):  
Neural Network ◽  
Parallel Processing ◽  
Convolutional Neural Network ◽  
Sign Language ◽  
Performance Metrics ◽  
Automatic Recognition ◽  
Upper Body ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Sign Languages

Sign Languages (SLs) are visual–gestural languages that have developed naturally in deaf communities. They are based on the use of lexical signs, that is, conventionalized units, as well as highly iconic structures, i.e., when the form of an utterance and the meaning it carries are not independent. Although most research in automatic Sign Language Recognition (SLR) has focused on lexical signs, we wish to broaden this perspective and consider the recognition of non-conventionalized iconic and syntactic elements. We propose the use of corpora made by linguists like the finely and consistently annotated dialogue corpus Dicta-Sign-LSF-v2. We then redefined the problem of automatic SLR as the recognition of linguistic descriptors, with carefully thought out performance metrics. Moreover, we developed a compact and generalizable representation of signers in videos by parallel processing of the hands, face and upper body, then an adapted learning architecture based on a Recurrent Convolutional Neural Network (RCNN). Through a study focused on the recognition of four linguistic descriptors, we show the soundness of the proposed approach and pave the way for a wider understanding of Continuous Sign Language Recognition (CSLR).

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