Cloud-based application for rice moisture content measurement using image processing technique and perceptron neural network

2017 ◽  
Author(s):  
Febus Reidj G. Cruz ◽  
Dionis A. Padilla ◽  
Carlos C. Hortinela ◽  
Krissel C. Bucog ◽  
Mildred C. Sarto ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Moisture Content ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Content Measurement ◽  
Moisture Content Measurement

Compact Disc Visual Inspection Using Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 727-732
Author(s):  
Anton Satria Prabuwono ◽  
Siti Rahayu Zulkipli ◽  
Doli Anggia Harahap ◽  
Wendi Usino ◽  
A. Hasniaty
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Visual Inspection ◽  
Processing Technique ◽  
Compact Disc ◽  
Image Processing Technique ◽  
Accuracy Rate ◽  
Network Training ◽  
Fields Of Study

Image processing is widely used in various fields of study including manufacturing as product inspection. In compact disc manufacturing, image processing has been implemented to recognize defect products. In this research, we implemented image processing technique as pre-processing processes. The aim is to acquire simple image to be processed and analyzed. In order to express the object from the image, the features were extracted using Invariant Moment (IM). Afterward, neural network was used to train the input from IM’s results. Thus, decision can be made whether the compact disc is accepted or rejected based on the training. Two experiments have been done in this research to evaluate 40 datasets of good and defective images of compact discs. The result shows that accuracy rate increased and can identify the quality of compact discs based on neural network training.

scholarly journals Diagnosing Chronic Glaucoma Using Watershed and Convolutional Neural Network

2021 ◽  
Vol 9 (9) ◽  
pp. 272-283
Author(s):  
Naureen Fathima
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Three Dimensional ◽  
Image Plane ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computational Technique ◽  
Fundus Imaging

Abstract: Glaucoma is a disease that relates to the vision of human eye,Glaucoma is a disease that affects the human eye's vision. This sickness is regarded as an irreversible condition that causes eyesight degeneration. One of the most common causes of lifelong blindness is glaucoma in persons over the age of 40. Because of its trade-off between portability, size, and cost, fundus imaging is the most often utilised screening tool for glaucoma detection. Fundus imaging is a two-dimensional (2D) depiction of the three-dimensional (3D), semitransparent retinal tissues projected on to the imaging plane using reflected light. The idea plane that depicts the physical display screen through which a user perceives a virtual 3D scene is referred to as the "image plane”. The bulk of current algorithms for autonomous glaucoma assessment using fundus images rely on handcrafted segmentation-based features, which are influenced by the segmentation method used and the retrieved features. Convolutional neural networks (CNNs) are known for, among other things, their ability to learn highly discriminative features from raw pixel intensities. This work describes a computational technique for detecting glaucoma automatically. The major goal is to use a "image processing technique" to diagnose glaucoma using a fundus image as input. It trains datasets using a convolutional neural network (CNN). The Watershed algorithm is used for segmentation and is the most widely used technique in image processing. The following image processing processes are performed: region of interest, morphological procedures, and segmentation. This technique can be used to determine whether or not a person has Glaucoma. Keywords: Recommender system, item-based collaborative filtering, Natural Language Processing, Deep learning.

Digitaization of Analogue Meter Reading Using Convolution Neural Network

Mekatronika ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 28-34
Author(s):  
Azmin Raziq Rizaman ◽  
Hazlina Selamat ◽  
Nurulaqilla Khamis
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Human Error ◽  
Processing Technique ◽  
Processing Method ◽  
Image Processing Technique ◽  
Use Case ◽  
High Tendency ◽  
Central Database ◽  
Domestic Electricity

Analogue meter is a device that has been widely used in a various industry to monitor and obtain the reading of the measurement. Based on the conventional approach, the meter reading will be done continuously by the meter reader that might cause high tendency of human error during the observation. To minimize this fallacy, this approach taken in this paper enables the automation of this the process by obtaining the reading from an analogue meter using an image processing technique and send the output to the central database for further processing. By implementing this approach, observation efficacy can be improved. This paper describes the process on how to obtain the digitized reading of an analogue meter using images captured by a camera. The images are then processed using an image processing method and the Convolutional Neural Network (CNN) is used to determine the reading of the meter. Data is then sent to the MySQL database, as this approach was easily implemented and managed either on-premises or via the cloud. The use case in this study was based on the analogue meter for domestic electricity supply in Malaysia and results show that the meter reading can accurately be recognized using the proposed approach.

scholarly journals Pneumonia Classification of Thorax Images using Convolutional Neural Networks

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 62
Author(s):  
Mahmud Suyuti ◽  
Endang Setyati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Technology Development ◽  
Image Data ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computing Technology ◽  
Correction Technique ◽  
High Level

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN).  The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.

scholarly journals Pneumonia Classification of Thorax Images using Convolutional Neural Networks

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 62-68
Author(s):  
Mahmud Suyuti ◽  
Endang Setyati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Technology Development ◽  
Image Data ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computing Technology ◽  
Correction Technique ◽  
High Level

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN).  The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.

A flexible image processing technique for measuring bubble parameters based on a neural network

2022 ◽  
Vol 429 ◽  
pp. 132138
Author(s):  
Yichuan He ◽  
Chengzhi Hu ◽  
Hongyang Li ◽  
Bo Jiang ◽  
Xianfeng Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Processing Technique ◽  
Image Processing Technique

scholarly journals CNN for Image Processing to Detect Weeds Using IOT

2021 ◽  
Vol 9 (11) ◽  
pp. 862-867
Author(s):  
Potta. Pavan Kumar
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Crucial Role ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Huge Amount ◽  
Weed Plants ◽  
Growth Habits ◽  
Water Nutrients ◽  
Day By Day

Abstract: One of the major issues in today’s agriculture fields is detecting weed plants in between the crops. Weeds consume more water, nutrients, and light compared to crop plants. Being hardy and vigorous in growth habits, they grow way to faster than crops and consume a huge amount of water and nutrients, results causing heavy losses in yields, the process of removal of weeds manually is a difficult job and it requires more manpower. To date, weed removal can’t be automated without manpower. Herbicides play a crucial role in removing the weeds but that leads to soil infertile and later the weeds dominate the field automatically. In solution to reduce the weeds is using herbicide in a higher amount than normal day by day. Usage of herbicides in that amount causes the land infertile. This paper deals with detecting the weeds in the crop using a convolutional neural network, Image processing, and IoT. The weeds in the field and between the crops are detected and removed by using the image processing technique. CNN algorithm is implemented in Matlab software to detect the weed areas in the fields. A robot model is connected to the controller through the motor driver which is also used to carry the camera through the field to detect the weed. The videos and images taken by the camera send to the Matlab and they are trained by using the CNN algorithm and that classifies whether it is a weed or a normal crop. And the necessary instructions send to the Arduino through Zigbee. If the camera detects any weed then the cutter is on 10 seconds to cut the weeds. And the robot model moves further until it finds the next weed. Users can also control the robot model whenever itneeds. Keywords: CNN; Weed cutter; Matlab; Zigbee; Image processing.

MOISTURE-DEPENDENT PHYSICAL PROPERTIES OF MEDICAL TINY SEEDS: PART B: SUMMER SAVORY (SATUREJA HORTENSIS L.), BASIL (OCIMUM BASILICUM L.), CRESS (LEPIDIUM SATIVUM L.), AND AJOWAN (CARUM COPTICUM L.)

2021 ◽  
Vol 24 (1) ◽  
Author(s):  
Amir Hossein Mirzabe ◽  
◽  
Ali Fadavi ◽  
Ali Mansouri ◽  
Ahmad Raufi ◽  
...  
Keyword(s):  
Image Processing ◽  
Moisture Content ◽  
Particle Density ◽  
Seed Mass ◽  
Processing Technique ◽  
Angle Of Repose ◽  
Image Processing Technique ◽  
Engineering Properties ◽  
Lepidium Sativum ◽  
Angle Of Friction

One of the most critical challenges of determining tiny seeds’ engineering properties is their dimensions. In the present study, a method based on digital image processing was employed to investigate the moisture content’s effect on geometric properties of summer savory, basil, cress, and ajowan seeds. Also, the gravimetrical and frictional properties of the seeds were measured. Gamma, Generalized Extreme Value, and Weibull distributions were used to model the probability frequency distribution of the seeds’ length, width, and thickness. This research indicated that the application of the image processing technique could be an excellent method to measure dimensional parameters of tiny seeds. Results showed that, with an increasing moisture content of seeds, the 1000-seed mass increased for basil, cress, and ajowan seeds; but, summer savory seeds showed that increasing-decreasing trend. For all four plant spices, increasing moisture content will result in the bulk density decrement. The particle density of summer savory, basil, and cress seeds decreased due to moisture content increment. However, the particle density of ajowan seeds increased with increasing moisture content. Results of measuring the angle of friction on iron, wood, and galvanized surfaces showed that, in most cases, the angle of friction increased by increasing moisture content. Also, except for the filling angle of repose of summer savory, increasing trends were observed for the emptying and filling angle of repose due to moisture content increment.

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