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 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).

scholarly journals KNOWLEDGE BASED 3D BUILDING MODEL RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS FROM LIDAR AND AERIAL IMAGERIES

2016 ◽  
Vol XLI-B3 ◽  
pp. 833-840 ◽  
Author(s):  
F. Alidoost ◽  
H. Arefi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Computation Time ◽  
3D Models ◽  
Recognition System ◽  
Automatic Recognition ◽  
Training Dataset ◽  
Feed Forward Neural Network ◽  
High Resolution Data ◽  
Building Roof

In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings’ roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings’ roofs automatically considering the complementary nature of height and RGB information.

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