scholarly journals Implementasi Pengolahan Citra Untuk Identifikasi Daun Tanaman Obat Menggunakan Levenberg-Marquardt Backpropagation

2021 ◽  
pp. 1-8
Author(s):  
Atsilfia Alfath Syam ◽  
Silfia Rifka ◽  
Siska Aulia
Keyword(s):  
Network Architecture ◽  
Aloe Vera ◽  
The Elderly ◽  
Recognition Algorithm ◽  
Identification System ◽  
Plant Identification ◽  
Betel Leaf ◽  
Reading Errors ◽  
Levenberg Marquardt ◽  
Offline Testing

Digital Image processing implementation can be applied to identify medicinal leaves, because it can help the elderly and people with color-blindness in identifying medicinal leave to be consumed and in avoiding reading errors, since some leaves have similar shape and color . In this discussion, the feature-extractions are using color and shape features, and using Levenberg-Marquardt for pattern recognition algorithm. The success of this medicinal plant identification system resulted in fairly good accuracy. The backpropagation network architecture used two hidden layers with 10 and 5 neurons. Data training is using 60 training leaf images with 15 images each of 5 types: green betel leaf, red betel, soursop, castor and aloe vera. Then, offline testing is using 20 test images for each of 4 images from 5 types with the accuracy of 85%. Meanwhile the online (realtime) test is using 20 times for each leaf types so the accuracy is 88%.

A DESIGN OF MOBILE ROBOT INTEGRATED RECOGNITION ALGORITHM TO ACCOMPANY THE ELDERLY

2021 ◽  
Author(s):  
The Tung Than ◽  
Tri Nhut Do ◽  
Hoai Nhan Nguyen ◽  
Minh Son Nguyen
Keyword(s):  
Mobile Robot ◽  
The Elderly ◽  
Recognition Algorithm

ARM-based Behavior Tracking and Identification System for Grouphoused Pigs

2019 ◽  
Vol 12 (6) ◽  
pp. 554-565
Author(s):  
Xingqiao Liu ◽  
Jun Xuan ◽  
Fida Hussain ◽  
Chen Chong ◽  
Pengyu Li
Keyword(s):  
Monitoring System ◽  
Complete Information ◽  
Recognition Algorithm ◽  
Detection Methods ◽  
Identification System ◽  
Monitoring Method ◽  
Embedded Platform ◽  
Food And Water Intake ◽  
Behavior Tracking

Background: A smart monitoring system is essential to improve the quality of pig farming. A real-time monitoring system provides growth, health and food information of pigs while the manual monitoring method is inefficient and produces stress on pigs, and the direct contact between human and pig body increases diseases. Methods: In this paper, an ARM-based embedded platform and image recognition algorithms are proposed to monitor the abnormality of pigs. The proposed approach provides complete information on in-house pigs throughout the day such as eating, drinking, and excretion behaviors. The system records in detail each pig's time to eat and drink, and the amount of food and water intake. Results: The experimental results show that the accuracy of the proposed method is about 85%, and the effect of the technique has a significant advantage over traditional behavior detection methods. Conclusion: Therefore, the ARM-based behavior recognition algorithm has certain reference significance for the fine group aquaculture industry. The proposed approach can be used for a central monitoring system.

scholarly journals Parallel Technique for Medicinal Plant Identification System using Fuzzy Local Binary Pattern

2017 ◽  
Vol 11 (1) ◽  
pp. 78-91
Author(s):  
N.N. Kutha Krisnawijaya ◽  
◽  
Yeni Herdiyeni ◽  
Bib Paruhum Silalahi ◽  
◽  
...  
Keyword(s):  
Medicinal Plant ◽  
Local Binary Pattern ◽  
Identification System ◽  
Plant Identification ◽  
Parallel Technique

scholarly journals The approach to the formation of the neural network architecture for pattern recognition

2016 ◽  
Vol 12 (2) ◽  
pp. 61-64 ◽  
Author(s):  
Vitaly M Tatyankin
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Network Architecture ◽  
Test Sample ◽  
Recognition Algorithm ◽  
Neural Network Architecture ◽  
Pattern Recognition Algorithm ◽  
The Neural Network

An approach to the formation of an efficient pattern recognition algorithm. Under efficiency, understood as a zero error, resulting in the identification of the images on the test sample. As a test sample is considered an open database of images of handwritten digits MNIST.

SPECIALIZED MEASURES OF LEAF OUTLINES AND HIERARCHICAL PERCEPTRONS IN AN AUTOMATIC IDENTIFICATION OF PLANTS

2004 ◽  
Vol 15 (08) ◽  
pp. 1171-1186 ◽  
Author(s):  
WOJCIECH BORKOWSKI ◽  
LIDIA KOSTRZYŃSKA
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Real Data ◽  
Automatic Identification ◽  
Identification System ◽  
Data Sets ◽  
Identification Algorithm ◽  
Plant Identification ◽  
Neural Network Classifiers ◽  
Computer Identification

The development of an efficient image-based computer identification system for plants or other organisms is an important ambitious goal, which is still far from realization. This paper presents three new methods potentially usable for such a system: fractal-based measures of complexity of leaf outline, a heuristic algorithm for automatic detection of leaf parts — the blade and the petiole, and a hierarchical perceptron — a kind of neural network classifier. The next few sets of automatically extractable features of leaf blades, encompassed those presented and/or traditionally used, are compared in the task of plant identification using the simplest known "nearest neighbor" identification algorithm, and more realistic neural network classifiers, especially the hierarchical. We show on two real data sets that the presented techniques are really usable for automatic identification, and are worthy of further investigation.

scholarly journals Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning

2019 ◽  
Vol 11 (24) ◽  
pp. 2997 ◽  
Author(s):  
Clément Dechesne ◽  
Sébastien Lefèvre ◽  
Rodolphe Vadaine ◽  
Guillaume Hajduch ◽  
Ronan Fablet
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architecture ◽  
Large Scale ◽  
Visual Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Human Observer ◽  
Convolutional Network ◽  
Ship Detection

The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries’ monitoring, maritime traffic surveillance, coastal and at-sea safety operations, and tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design a multi-task neural network architecture composed of one joint convolutional network connected to three task specific networks, namely for ship detection, classification, and length estimation. The experimental assessment shows that our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m).

scholarly journals DNA Barcoding as an Effective Tool in Improving a Digital Plant Identification System: A Case Study for the Area of Mt. Valerio, Trieste (NE Italy)

PLoS ONE ◽  
2012 ◽  
Vol 7 (9) ◽  
pp. e43256 ◽  
Author(s):  
Ilaria Bruni ◽  
Fabrizio De Mattia ◽  
Stefano Martellos ◽  
Andrea Galimberti ◽  
Paolo Savadori ◽  
...  
Keyword(s):  
Dna Barcoding ◽  
Identification System ◽  
Plant Identification ◽  
Ne Italy ◽  
System A
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