Quality Detection and Specie Identification of Apples Based on Multi-Spectral Imaging

2011 ◽  
Vol 301-303 ◽  
pp. 158-164 ◽  
Author(s):  
Chun Xiao Tang ◽  
En Bang Li ◽  
Chuan Zhen Zhao ◽  
Chao Li
Keyword(s):  
Spectral Imaging ◽  
Identification System ◽  
Grading System ◽  
Infrared Images ◽  
Accuracy Rate ◽  
Quality Detection ◽  
Fruit Grading ◽  
Fuji Apple ◽  
Grading Standards ◽  
Apple Quality

This paper introduced an apple quality detection and specie identification system based on multi-spectral imaging. Under an international mixed light illumining, system can capture red, green and infrared images of apples at the same time. A software programmed based on Matlab 6.5.1 is used for image processing to complete the detection of quality and specie. According to processing results, the subtotals and classification are made into grading standards. These can be quickly and easily applied to the automation of agriculture fruit grading system. In the experiment, some most common apples including Fuji apple, Red delicious apples, Green apples, Gina Apple's were detected for quality and variety . Accuracy rate can be more than 90%.

scholarly journals Speech Synthesis for Gender Classification

2017 ◽  
Vol 5 (1) ◽  
pp. 67
Author(s):  
Kawthar AlDhlan
Keyword(s):  
Speech Synthesis ◽  
Support Vector ◽  
Levenshtein Distance ◽  
Identification System ◽  
Gender Identification ◽  
Accuracy Rate ◽  
Kingdom Of Saudi Arabia ◽  
Svm Model ◽  
Health Related ◽  
Test Flow

<p class="0abstract">This paper presents a gender identification system to be used for call forwarding in health related communications. The system listens to the caller then using speech synthesis, image processing, and linear support vector machine SVM identifies either he or she is a male or a female. This solution is imperative in a conservative country such as the Kingdom of Saudi Arabia in order to forward the call to a male or female practitioner. The originality of the approach is that no transcription is used to learn SVM models. To identify the gender of the caller, the trained SVM model of the reference pieces are compared to transcripts of the audio frequency record and are using the Levenshtein distance. For the identification of gender, we obtain an accuracy rate of 94% on a test flow containing 449 pieces of speech clips.</p>

scholarly journals A Novel Apple Size and Surface Quality Detection and Grading System

2019 ◽  
Vol 18 (3) ◽  
pp. 237-242
Author(s):  
Li Liu ◽  
Xin Qiao ◽  
Xindong Shi ◽  
Qunming Liu ◽  
Yinggang Shi
Keyword(s):  
Surface Quality ◽  
Grading System ◽  
Quality Detection

Features Extraction for Eggplant Fruit Grading System Using Machine Vision

2008 ◽  
Vol 24 (5) ◽  
pp. 675-684 ◽  
Author(s):  
V. K. Chong ◽  
N. Kondo ◽  
K. Ninomiya ◽  
T. Nishi ◽  
M. Monta ◽  
...  
Keyword(s):  
Machine Vision ◽  
Features Extraction ◽  
Grading System ◽  
Fruit Grading

EVALUATION OF NORMAL AND MODIFIED ASPHALT ACCORDING TO CONVENTIONAL AND MODERN GRADING SYSTEMS

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Maram Saudy ◽  
Safwan Khedr
Keyword(s):  
High Performance ◽  
High Reliability ◽  
Grading System ◽  
Modified Asphalt ◽  
Grading Systems ◽  
Asphalt Performance ◽  
Grading Standards ◽  
High Level ◽  
Maintenance Requirements ◽  
Different Sources

Asphalt plays a significant role in pavement quality. The need for high-performance pavements with long service life and low maintenance requirements is the motive behind thorough research and studies of asphalt characteristics. This research focuses on studying all sources of Egyptian asphalt over a span of time using both conventional and Superpave grading techniques in order to characterize asphalt performance and also to answer the question whether the Egyptian asphalts need modification. The results of this research indicate that all Egyptian normal (virgin) 60/70 asphalt samples from different sources failed to meet penetration grading requirements, viscosity grading standards AC-20 (high quality); with minor exceptions, viscosity grading system AC-20 (low quality), and both AR-8000 and AR-1000 Aged Residue grading systems. When Superpave grading system was employed, results indicate that all normal asphalt samples failed to meet the basic requirements (without traffic adjustment) according to the Egyptian climatic requirements for high reliability projects (PG70-10 and PG76-10). The testing results accommodate Superpave requirements for lower levels of reliability and/or lower level of conservativeness. This emphasizes the flexibility and reliability of Superpave grading system as compared to conventional grading systems. On the other hand all modified asphalt samples, using an SBS modifier, passed according to the base high reliability projects and/or high level of conservativeness requirements of the Superpave grading system. Finally it is concluded that Egyptian asphalt should be modified in order to provide satisfactory performance especially for high reliability projects in hot regions with high and/or slow traffic.

scholarly journals SPEAKER IDENTIFICATION MENGGUNAKAN TRANSFORMASI WAVELET DISKRIT DAN JARINGAN SARAF TIRUAN BACK-PROPAGATION

2008 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Anny Tandyo ◽  
Martono Martono ◽  
Adi Widyatmoko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Back Propagation ◽  
Identification Accuracy ◽  
Identification System ◽  
Accuracy Rate ◽  
Discrete Transformation ◽  
Artificial Neural

Article discussed a speaker identification system. Which was a part of speaker recognition. The system identified asubject based on the voice from a group of pattern had been saved before. This system used a wavelet discrete transformationas a feature extraction method and an artificial neural network of back-propagation as a classification method. The voiceinput was processed by the wavelet discrete transformation in order to obtain signal coefficient of low frequency as adecomposition result which kept voice characteristic of everyone. The coefficient then was classified artificial neural networkof back-propagation. A system trial was conducted by collecting voice samples directly by using 225 microphones in nonsoundproof rooms; contained of 15 subjects (persons) and each of them had 15 voice samples. The 10 samples were used as atraining voice and 5 others as a testing voice. Identification accuracy rate reached 84 percent. The testing was also done onthe subjects who pronounced same words. It can be concluded that, the similar selection of words by different subjects has noinfluence on the accuracy rate produced by system.Keywords: speaker identification, wavelet discrete transformation, artificial neural network, back-propagation.

scholarly journals Implementasi Metode Feature Extraction pada Klasifikasi Kualitas Daun Tembakau Madura

Rekayasa ◽  
2017 ◽  
Vol 10 (2) ◽  
pp. 71
Author(s):  
Kunto Aji Wibisono ◽  
Achmad Fiqhi Ibadillah
Keyword(s):  
Economic Value ◽  
Raw Material ◽  
Grading System ◽  
Leaf Quality ◽  
Tobacco Leaf ◽  
Quality Detection ◽  
Quality Classification ◽  
Accuracy Level

<p>Madura merupakan salah satu daerah penghasil tembakau di Indonseia. Tembakau Madura  merupakan jenis komoditi perkebunan yang memiliki nilai ekonomi tinggi. Sebagian besar tembakau madura diserap oleh pabrik rokok sebagai bahan baku utama rokok maupun sebagai racikan atau campuran kretek. Secara umum tembakau Madura sendiri dibagi menjadi tiga bagian: tembakau gunung, tembakau tegal, dan tembakau sawah. Jenis tembakau gunung adalah yang paling diburu oleh pabrik rokok, meski produktivitasnya terbilang sangat rendah dibanding tembakau sawah. Terdapat banyak jenis  varietas tembakau gunung yang ditanam petani di Madura, namun  yang memiliki karakteristik khas adalah tembaku Prancak – 95. Hal ini disebabkan  <a href="http://global-news.co.id/2016/04/tembakau-varietas-prancak-95-madura-diam-diam-dikembangkan-tiongkok/">Aroma tembakau Prancak-95 Madura tidak bisa ditiru oleh</a> jenis varietas tembaku lain di Indonesia. Hal lain yang membedakan yaitu terjadi karena kontur atau struktur tanah Madura yang memang khas, yang merupakan kelebihan dari tembakau Madura.Pada penelitian ini didesain sebuah sistem gradding untuk mendeteksi kualitas tembakau Prancak – 95 madura. Deteksi kualitas daun tembakau ini didasarkan pada dua ekstraksi fitur yaitu tekstur dan aromatik. Berdasarkan kedua fitur tersebut nantinya akan diklasifikasikan dengan menggunakan standard kualifikasi SNI. Sehingga  level akurasi deteksi kualitas daun tembakau Madura menjadi lebih optimal</p><p class="Normal1"><em>Kata Kunci: </em><em>Image extraction, Sensor Gas, Tembakau Madura</em><em>.</em></p><p class="Normal1" align="center">Implementation of Feature Image Extraction on Quality Classification of Maduraness Tobacco<strong></strong></p><p class="Normal1"><strong>ABSTRACT</strong></p><p><em>Madura is one of the tobacco producing areas in Indonesian. Madura tobacco is a type of plantation commodity that has high economic value. Most tobacco Madura is absorbed by cigarette manufacturers as the main raw material of cigarettes as well as as a concoction or clove mixture. In general Madura tobacco itself is divided into three parts: mountain tobacco, tobacco tegal, and tobacco sawah. Types of mountain tobacco are the most hunted by cigarette manufacturers, although the productivity is very low compared to tobacco. There are many types of varieties of mountain tobacco grown by farmers in Madura, but which has a distinctive characteristic is the Prancak-95 tobacco. This is because the Prancak-95 Madura tobacco aroma cannot be imitated by other types of copious varieties in Indonesia. Another thing that distinguishes that occurs due to the contour or structure of Madura land that is typical, which is the advantage of Madura tobacco. In this study designed a grading system to detect the quality of Prancak tobacco - 95 madura. The tobacco leaf quality detection is based on two feature extractions, namely texture and aromatics. Based on these two features will be classified using SNI qualification standards. So that the accuracy level of Madura tobacco leaf quality detection becomes more optimal</em><em></em></p><em>Keywords: Image extraction, Gas Sensor, Maduraness Tobacco </em>

Double-channel on-line automatic fruit grading system based on computer vision

2007 ◽  
Author(s):  
Junxiong Zhang ◽  
Yi Xun ◽  
Wei Li ◽  
Cong Zhang
Keyword(s):  
Computer Vision ◽  
Grading System ◽  
Fruit Grading ◽  
On Line ◽  
Double Channel

A Novel Hybrid Metaheuristic Approach to Perceive Gender Based Identification System

2020 ◽  
Vol 13 ◽  
Author(s):  
Aparna Shukla ◽  
Suvendu Kanungo
Keyword(s):  
Search Space ◽  
Recognition System ◽  
High Dimensional ◽  
Identification System ◽  
Hybridization Technique ◽  
Optimization Approach ◽  
Gender Recognition ◽  
Accuracy Rate ◽  
Tree Classifier ◽  
Gender Based

Background: Gender recognition is one of the most challenging perceptible tasks that receiving attention in the increasing digital data era as the requirement of personalized, reliable and ethical system inevitable. A problem that we address in this paper, greatly deals with the gender based identification system. We are motivated by this problem as many recent social interactions and existing services rely on the gender of an individual, and also in forensic identification, the gender information provides the feasibility for easy and quick investigation. Objective: The paper primarily focused on the gender based identification problem and culminate a robust gender based recognition system with the higher accuracy rate. We attempted to perceive the gender of an individual through the multimodal biometric system by integrating the three prominent biometric traits namely: fingerprint, palm-print and hand in a specific manner. The proposed multimodal biometric for gender recognition system provides a better accuracy rate improvement with the optimal feature set which are generated from available high dimensional features set. Method: Aiming for the objective to reduce the search space, a hybrid meta-heuristic approach GSA-Firefly (GFF) is introduced in this paper. The optimization approach GFF is proposed to retrieve the optimal number of features from the high dimensional features generated by fusing the texture features of all the three considered biometric traits along with the fingerprint minutiae features. Further, the decision tree classifier is used to classify the gender of an individual. Results: The feasibility of the proposed approach is measured with different qualitative performance parameters. In light of achieving the accuracy rate of 99.2%, it shows that its performance comparatively better against other techniques reported in the literature with the different sets of classier. Conclusion: The hybridization technique that effectively integrate meta-heuristic approaches GSA and firefly outperforms other similar approaches with respect to obtaining the optimal features of multimodal biometric for gender based identification system. Further, the novel technique enhance the overall performance of the system by reducing the search space over time and space.

Quality Identification: Fruit Grading System

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Monali Chinchamalatpure
Keyword(s):  
Image Processing ◽  
Economic Development ◽  
Grading System ◽  
Image Processing Techniques ◽  
Smart Farming ◽  
Fruit Grading ◽  
Cost Efficient ◽  
Time Required ◽  
Processing Techniques

In India, agriculture plays a major role in the economic development. Agriculture must be able to produce fruit of better quality and grow at a faster rate. With the use of different image processing techniques, improvement in agriculture field for quality identification, sorting the fruits with different quality, irrigation becomes feasible. Major parameters considered are reduction in the time required and cost efficient, using Image processing. In this proposal, we have provided a technique to address the challenge of fruit grading using image processing techniques for smart farming.

Automated Grading of Tomatoes Using Artificial Intelligence

2020 ◽  
pp. 216-239
Author(s):  
Tawanda Mushiri ◽  
Liberty Tende
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Processing ◽  
Tomato Fruit ◽  
Grading System ◽  
Past Century ◽  
Manual Sorting ◽  
Fruit Processing ◽  
Automated Grading ◽  
Fruit Grading

The rate of production of horticultural produce had been seen increasing from the past century owing to the increase of population. Manual sorting and grading of tomatoes had become a challenge in market places and fruit processing firms since the demand of the fruit had increased. Considering grading of tomatoes, color is of major importance when it comes to the maturity of the tomatoes. Hence, there is a need to accurately classify them according to color. This process is very complicated, tiresome, and laborious when it is done manually by a human being. Apart from being labor-demanding, human sorting, and grading results in inaccuracy in classifying of tomatoes which is a loss to both the farmer and customer. This chapter had been prepared focusing on the automatic and effective tomato fruit grading system using artificial intelligence particularly using artificial neural network in Matlab. The system makes use of the image processing toolbox and the ANN toolbox to process and classify the tomatoes images according to color and size.

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