Computer aided plant identification system

2013 ◽  
Author(s):  
Ngoc-Hai Pham ◽  
Thi-Lan Le ◽  
Pierre Grard ◽  
Van-Ngoc Nguyen
Keyword(s):  
Identification System ◽  
Plant Identification ◽  
Computer Aided

SMoBAICS

2017 ◽  
Vol 5 (2) ◽  
pp. 34-57
Author(s):  
Volkhard Klinger
Keyword(s):  
Data Acquisition ◽  
Simulation Models ◽  
Identification System ◽  
Animal Testing ◽  
Cuff Electrode ◽  
Platform Architecture ◽  
Computer Aided ◽  
Verification Systems ◽  
Application Fields ◽  
And Control

Simulation and modelling are powerful methods in computer aided therapy, rehabilitation monitoring, identification and control. The smart modular biosignal acquisition and identification system (SMoBAICS) provides methods and techniques to acquire electromyogram (EMG)- and electroneurogram (ENG)-based data for the evaluation and identification of biosignals. In this paper the author focuses on the development, integration and verification of platform technologies which support this entire data processing. Simulation and verification approaches are integrated to evaluate causal relationships between physiological and bioinformatical processes. Based on this we are stepping up of efforts to develop substitute methods and computer-aided simulation models with the objective of reducing animal testing. This work continues the former work about system identification and biosignal acquisition and verification systems presented in (Bohlmann et al., 2010), (Klinger and Klauke, 2013), (Klinger, 2014). This paper focuses on the next generation of an embedded data acquisition and identification system and its flexible platform architecture. Different application scenarios are shown to illustrate the system in different application fields. The author presents results of the enhanced closed-loop verification approach and of the signal quality using the Cuff-electrode-based ENG-data acquisition system.

scholarly journals Computer-aided photo-identification system with an application to polar bears based on whisker spot patterns

2010 ◽  
Vol 91 (6) ◽  
pp. 1350-1359 ◽  
Author(s):  
Carlos J. R. Anderson ◽  
Niels Da Vitoria Lobo ◽  
James D. Roth ◽  
Jane M. Waterman
Keyword(s):  
Identification System ◽  
Polar Bears ◽  
Photo Identification ◽  
Computer Aided

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 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%.

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 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

scholarly journals Predicting the Crop Type and Region of Abundance using Machine Learning

2021 ◽  
Vol 9 (9) ◽  
pp. 1411-1416
Author(s):  
Mohd Khalid Shaikh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Rural Area ◽  
Identification System ◽  
Plant Identification ◽  
Convolution Neural Networks ◽  
Technology Students ◽  
Modern Age ◽  
Crop Type ◽  
As If

Abstract: In this modern age of science too technology, students and people in big cities ignorance of many things, such as how we get food, how things are processed, and much more. We are just it focuses on the results we get, because of this morality our knowledge diminishes, as if we did not know the crops or the goods ourselves using. As we visit the rural area when we arrive beyond some kind of plant, we can't know that, so we have identified this place to resolve the problem of students, researchers and many more people by creating a plant identification system which will predict the type of crop and the location of abundance where the harvest is planted. Keywords: Crop Identification System, Convolution Neural networks, MobilenetV2.

Sign in / Sign up

Export Citation Format

Share Document