scholarly journals Sistem Deteksi Cepat Mutu Organoleptik Beras Berbasis Android

2020 ◽  
Vol 9 (4) ◽  
pp. 167-174
Author(s):  
Mulyana Hadipernata ◽  
Agus Supriatna Somantri ◽  
Maulida Hayuningtyas ◽  
Nikmatul Hidayah ◽  
Hoerudin Hoerudin
Keyword(s):  
Image Processing ◽  
Rapid Detection ◽  
Amylose Content ◽  
Detection System ◽  
Android Application ◽  
Rice Varieties ◽  
Organoleptic Quality ◽  
Detection Tool ◽  
Image Characteristics

Penelitian ini bertujuan untuk mengembangkan alat deteksi cepat mutu organoleptik beras berbasis pada pemanfaatan aplikasi Android agar pengujian mutu organoleptik beras dapat dilakukan secara cepat dan akurat. Bahan penelitian yang digunakan adalah beras varietas Ciherang dan Tarabas. Metode yang digunakan adalah dengan menggunakan realtime image processing berbasis Android dan Java. Hasil penelitian menunjukkan bahwa lamanya penyimpanan beras sangat mempengaruhi citra beras (Red Green Blue/RGB). Selama penyimpanan beras, nilai Blue menghasilkan nilai perubahan yang nyata dibandingkan nilai Red dan Green. Nilai Blue ini berkorelasi positif terhadap perubahan kadar amilosa selama penyimpanan dan mutu organoleptiknya. Aplikasi deteksi cepat mutu organoleptik beras juga telah berhasil dibuat dan dapat diuji validitasnya dengan memperhatikan perubahan karakateristik citra, perubahan amilosa, dan mutu organoleptiknya. Kesimpulannya, aplikasi deteksi cepat ini berhasil dikembangkan dengan berbasis Android yang dapat digunakan sebagai alat uji mutu organoleptik berasRapid Detection System for Organoleptic Quality of Rice using the Android ApplicationAbstractThe research was aimed at developing rapid detection tool of rice upon organoleptic quality based on the Android application, so the testing may be done quickly and accurately. Ciherang and Tarabas rice varieties were used in this research. Realtime image processing based on Android and Java were used as method in this research. The results showed that the storage affected the rice image value (Red Green Blue/RGB). During storage, the value of the blue (B) produced a proper marked which was positively correlated to the changes in amylose content. Application of rapid detection of organoleptic quality of rice was carried out by observing changes in image characteristics, changes in amylose, and changes in organoleptic properties. As conclusion, the application may functioning properly and can be used as a tool to test the organoleptic quality of rice and its shelf life.

scholarly journals The Distribution of Mineral Elements and Protein in Soft Rice was Determined and Analyzed by SEM-EDS Technology

2021 ◽  
pp. 39-50
Author(s):  
Bo Peng ◽  
Xiao-Rui Ma ◽  
Wen-Ting Cui ◽  
Xia-Yu Tian ◽  
Chao Dong ◽  
...  
Keyword(s):  
Amylose Content ◽  
Energy Dispersive Spectrometer ◽  
Sweet Taste ◽  
Mineral Elements ◽  
Limiting Factor ◽  
Rice Seed ◽  
High Quality ◽  
Rice Varieties ◽  
Depth Analysis

Soft rice is a kind of high-quality rice between glutinous rice and sticky rice. It has low amylose content, crystal clear grains, sweet taste, soft glutinous, and is suitable for cooking and porridge. Chalkiness in soft rice is a white opaque part formed by loose endosperm. It is an important character that affects the appearance quality, processing, and cooking quality of rice, and also an important limiting factor that restricts the standard rate of high-quality rice in China. The combination of scanning electron microscope and energy dispersive spectrometer (SEM-EDS) can be used for in-depth analysis of rice, visualization, and quantitative analysis of element distribution in rice. The results showed that there were many kinds of mineral elements in soft rice seeds, among which C and O were the most abundant, followed by N and P, and Mg, Al, P, S, K, Ca, Mn, and Zn were less. The contents of C, N, P, and S in the non-chalky area were significantly higher than those in the chalky area. Especially N and S were the best indicators of protein, and the contents in the chalky area were higher than those in the non-chalky area. It means that the protein content in the chalky part of soft rice seed is less than that in the non-chalky part, which affects the nutritional quality of soft rice. Therefore, the results of this study laid a solid foundation for the in-depth analysis of the distribution of mineral elements and protein in soft rice and their effects on the quality of soft rice, which also provided important information for the cultivation of new high-quality rice varieties in the future.

scholarly journals A Colour Perception Detection Model Based on The Spectral Characterictics

2019 ◽  
Vol 4 (2) ◽  
pp. 87
Author(s):  
Irwan Anto Mina
Keyword(s):  
Information Needs ◽  
Euclidean Distance ◽  
Color Image ◽  
Detection System ◽  
Color Perception ◽  
Blue Color ◽  
Colour Perception ◽  
Detection Tool ◽  
Average Color

<p><em>Information needs for one's color perception are needed in the fields of medicine, engineering, astronomy, biomedicine and so on. The demand for accurate assessment of color perception must be met by the perception detection tool used. Ishihara's test, as a perception detection tool that is still used today has insufficient accuracy. This research aims to create a system that can detect a shift in one's color perception, relative to the average color perception of a number of respondents. Through plotting the respondents' perception points, in the CIE coordinate system (Commission International de I'Eclairage) XYZ can be calculated the average euclidean distance, ED, relative to the reference point and the distribution of x and y groups of perception points around the point of reference. Both size, euclidean distance and distribution are used as indicators of average color perception so that an assessment of one's color perception is given based on the results of comparison between color perception points and color perception indicators. The tool used to do the test is Delphi version 7.0 software. the research material used is the RGB (Red, Green, Blue) color image format. The results of a person's color perception study are divided into three levels, namely: (1) "normal" assessment if euclidean (ED) perceptions are smaller than the euclidean (ED) average (2) the "somewhat normal" assessment if the distribution of x and y is smaller rather than the color of perception and the distribution of x and y (3) the assessment is "abnormal" if the color of perception is greater than the max distribution of x and y. A new perception point assessment that is in level one is used to up-date prevailing perception indicators. Up-dating condition constraints affect the quality of the threshold average perception specifically and the quality of the results of the perception detection system in general.</em></p>

scholarly journals Digital Image Based Identification of Rice Variety Using Image Processing and Neural Network

2015 ◽  
Vol 16 (1) ◽  
pp. 182
Author(s):  
Lilik Sumaryanti ◽  
Aina Musdholifah ◽  
Sri Hartati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Classification Accuracy ◽  
Rice Variety ◽  
Texture Features ◽  
Rice Varieties ◽  
Food Grains ◽  
Average Accuracy ◽  
Neural Network Algorithm

The increased of consumer concern on the originality of rice  variety and the quality of rice leads to originality certification of rice by existing institutions. Technology helps human to perform evaluations of food grains using images of objects. This study developed a system used as a tool to identify rice varieties. Identification process was performed by analyzing rice images using image processing. The analyzed features for identification consisted of six color features, four morphological features, and two texture features. Classifier used LVQ neural network algorithm. Identification results using a combination of all features gave average accuracy of 70,3% with the highest classification accuracy level of 96,6% for Mentik Wangi and the lowest classification accuracy of 30%  for Cilosari.

scholarly journals Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image

2020 ◽  
Vol 10 (10) ◽  
pp. 3371 ◽  
Author(s):  
Jun Fu ◽  
Haikuo Yuan ◽  
Rongqiang Zhao ◽  
Zhi Chen ◽  
Luquan Ren
Keyword(s):  
Image Processing ◽  
Theoretical Basis ◽  
Detection System ◽  
Post Processing ◽  
Recognition Method ◽  
Damage Recognition ◽  
Working Parameters ◽  
Corn Kernels ◽  
Rgb Image

Corn ear damage caused by peeling significantly influence the output and quality of corn harvest. Ear damage recognition is the basis to adjust working parameters and to reduce damage. Image processing is attracting increasing attentions in the field of agriculture. Conventional image processing methods are difficult to be used for recognizing corn ear damage caused by peeling in field harvesting. To address the this problem, in this paper, we propose a peeling damage recognition method based on RGB image. For our method, we develop a dictionary-learning-based method to recognize corn kernels and a thresholding method to recognize ear damage regions. To obtain better performance, we also develop the corroding algorithm and the expanding algorithm for the post-processing of recognized results. The experimental results demonstrate the practicality and accuracy of the proposed method. This study could provide the theoretical basis to develop online peeling damage detection system for corn ear harvesters.

Physical, Cooking and Organoleptic Quality of Some Rice Varieties of Eastern Uttar Pradesh

2015 ◽  
Vol 9 (1) ◽  
pp. 23-25
Author(s):  
Sadhna Singh ◽  
Uttra Singh ◽  
Ajit Vats ◽  
Renu Verma ◽  
Stuti Srivastava
Keyword(s):  
Uttar Pradesh ◽  
Rice Varieties ◽  
Organoleptic Quality ◽  
Eastern Uttar Pradesh

scholarly journals Quality Detection System of Transparent Nonel Tubes Based on Image Processing

2015 ◽  
Vol 9 (1) ◽  
pp. 697-702
Author(s):  
Guodong Sun ◽  
Wei Xu ◽  
Lei Peng
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Detection System ◽  
Recognition Rate ◽  
Human Vision ◽  
Detection Methods ◽  
Detection Accuracy ◽  
External Diameter ◽  
Quality Detection

The traditional quality detection method for transparent Nonel tubes relies on human vision, which is inefficient and susceptible to subjective factors. Especially for Nonel tubes filled with the explosive, missed defects would lead to potential danger in blasting engineering. The factors affecting the quality of Nonel tubes mainly include the uniformity of explosive filling and the external diameter of Nonel tubes. The existing detection methods, such as Scalar method, Analysis method and infrared detection technology, suffer from the following drawbacks: low detection accuracy, low efficiency and limited detection items. A new quality detection system of Nonel tubes has been developed based on machine vision in order to overcome these drawbacks. Firstly the system architecture for quality detection is presented. Then the detection method of explosive dosage and the relevant criteria are proposed based on mapping relationship between the explosive dosage and the gray value in order to detect the excessive explosive faults, insufficient explosive faults and black spots. Finally an algorithm based on image processing is designed to measure the external diameter of Nonel tubes. The experiments and practical operations in several Nonel tube manufacturers have proved the defect recognition rate of proposed system can surpass 95% at the detection speed of 100m/min, and system performance can meet the quality detection requirements of Nonel tubes. Therefore this quality detection method can save human resources and ensure the quality of Nonel tubes.

scholarly journals Identification of Feasibility of Canned Based Foods Image Processing Techniques Using Thresholding

2021 ◽  
Vol 3 (1) ◽  
pp. 10
Author(s):  
Ilhamsyah Muhammad Nurdin ◽  
Abdul Fadlil
Keyword(s):  
Image Processing ◽  
Visual Observation ◽  
Android Application ◽  
File Transfer Protocol ◽  
File Transfer ◽  
Canned Food ◽  
Image Processing Techniques ◽  
Accuracy Level ◽  
Processing Techniques

Eye sight is sometimes deceptive, especially in determining the quality of a canned food, so it is necessary to use technology that resembles human visual observation, namely in the form of an application. The process to detect the quality of canned food uses image processing methods, especially thresholding, which is then designed so that the application is able to determine the quality of canned food with the help of the MATLAB GUI which detects and then sends it from making the MATLAB GUI on the Laptop to Android using FTP (File Transfer Protocol). At the end of the process, it is marked with known good and bad quality of canned food with an android application that has been specially designed with an accuracy level of 84% with a thresholding value of 70.

scholarly journals Relationship between High Temperature and Formation of Chalkiness and Their Effects on Quality of Rice

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
A. Y. M. Nevame ◽  
R. M. Emon ◽  
M. A. Malek ◽  
M. M. Hasan ◽  
Md. Amirul Alam ◽  
...  
Keyword(s):  
High Temperature ◽  
Amylose Content ◽  
Pragmatic Approach ◽  
Rice Varieties ◽  
Material Resources ◽  
Temperature Responsive ◽  
Stable Qtls ◽  
Gel Consistency ◽  
Genetic And Environmental Factors

Occurrence of chalkiness in rice is attributed to genetic and environmental factors, especially high temperature (HT). The HT induces heat stress, which in turn compromises many grain qualities, especially transparency. Chalkiness in rice is commonly studied together with other quality traits such as amylose content, gel consistency, and protein storage. In addition to the fundamental QTLs, some other QTLs have been identified which accelerate chalkiness occurrence under HT condition. In this review, some of the relatively stable chalkiness, amylose content, and gel consistency related QTLs have been presented well. Genetically, HT effect on chalkiness is explained by the location of certain chalkiness gene in the vicinity of high-temperature-responsive genes. With regard to stable QTL distribution and availability of potential material resources, there is still feasibility to find out novel stable QTLs related to chalkiness under HT condition. A better understanding of those achievements is essential to develop new rice varieties with a reduced chalky grain percentage. Therefore, we propose the pyramiding of relatively stable and nonallelic QTLs controlling low chalkiness endosperm into adaptable rice varieties as pragmatic approach to mitigate HT effect.

scholarly journals The object detection system of balinese script on traditional Balinese manuscript with findcontours method

2021 ◽  
Vol 11 (3) ◽  
pp. 177-184
Author(s):  
Putra Manuaba ◽  
◽  
Komang Ayu Triana Indah ◽  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Edge Detection ◽  
Object Detection ◽  
Detection System ◽  
Main Issue ◽  
Age Factor

Lontar is a traditional Balinese manuscript with a Balinese script in it. Balinese traditional manuscripts can be more than 100 years old. The age factor of the Balinese manuscript has an impact on the Balinese script in it. Balinese script that has been written more than 10 years tends to be darker. This makes Balinese script not visible well, and this affects the image quality of the manuscript. This thing becomes the main issue in this research, Balinese script detection on Balinese manuscript images. the first of all is image processing using edge detection, canny and Sobel becomes the main algorithm of this process. After image processing, the Balinese manuscript will be processed with the findcontour method to detect an object that contains in it. The final process of this detection system is to separate detected objects into three main groups namely noise object, Balinese script object, and hole object. Application (Balinese script object detection system) is more accurate in detecting Balinese script objects in Balinese script under 1 year (new script), it tends to be more likely to find noise/dirt. This is because the writing of the lontar using a pencil first before using the knife media. This adds to the noise or dirt detected by the application The findcontour method can detect Balinese script objects with a detection result of 30% - 70% Balinese script objects.

scholarly journals The Effect of Mocaf Substitution on Crispness and Organoleptic Quality of Milk Sticks

JURNAL TERNAK ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 49
Author(s):  
Dyah Nurul Afiyah ◽  
Riska Nurtyanto Sarbini
Keyword(s):  
Wheat Flour ◽  
Amylose Content ◽  
Animal Science ◽  
Organoleptic Quality ◽  
Randomized Design ◽  
Completely Randomized Design ◽  
Quality Of Milk ◽  
High Amylose ◽  
Made In

Milk sticks are one of the dairy products that are served in the form of snacks. One of the ingredients added in making milk sticks is wheat flour which has a high amylose content. It causes the resulting milk sticks to be less crunchy. It is necessary to substitute flour with low amylose content, namely mocaf (Modified Cassava Flour). This research objective was to determine the effect of mocaf on the level of crispness and organoleptic quality of milk sticks. Milk sticks was made in animal science laboratory UNISKA Kediri by adding different percentage of flour: P0 (100% wheat flour), P1 (90% wheat flour and 10% mocaf), P2 (80% wheat flour and 20% mocaf), P3 (70% wheat flour and 30% mocaf), and P4 (60% wheat flour and 40% mocaf) in six replications. This research using completely randomized design (CRD). These results indicated that the substitution of mocaf in the manufacture of milk sticks could reduce the moisture content of the milk sticks so that the substitution of mocaf could increase the crispness. The organoleptic assessment was carried out on the parameters of color, taste, and crispness. There were no significant differences between the color and taste parameters, while the crispness showed that P0 was not significantly different from P1, but it was significantly different from P2, P3, and P4.

Sign in / Sign up

Export Citation Format

Share Document