scholarly journals Implementasi Jaringan Saraf Tiruan Sebagai Alat Bantu Deteksi Bakteri Staphylococcus Aureus Pada Sayuran

2020 ◽  
Vol 1 (3) ◽  
pp. 258
Author(s):  
Ali Rahmad Pohan
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Staphylococcus Aureus ◽  
Image Segmentation ◽  
Artificial Neural Networks ◽  
Image Quality ◽  
Bacterial Detection ◽  
Image Characteristics ◽  
Reduce Image Quality ◽  
Artificial Neural

This study aims to aid bacterial detection through bacterial imagery in vegetables to help identify Staphylococcus aureus bacteria in vegetables. Input to the software is the image of bacteria in vegetables. Bacterial image is processed by grayscaling, thresholding and image segmentation processing methods so that the image characteristics that represent bacteria in vegetables are obtained. One technique that can be used as a tool to observe Staphylococcus aureus is to use artificial neural networks and combine them with image processing. Artificial neural networks function as information processing by inferring information from data that has been received and as a decision maker for data that has been studied. Image processing is the science of manipulating images, which includes techniques to improve or reduce image quality. The detection process using software that has been built can be done well. The process is carried out by matching the value of the exercise cutra backpropagation vector with the image to be detected.

scholarly journals Use of artificial neural networks and geographic objects for classifying remote sensing imagery

CERNE ◽  
2014 ◽  
Vol 20 (2) ◽  
pp. 267-276 ◽  
Author(s):  
Pedro Resende Silva ◽  
Fausto Weimar Acerbi Júnior ◽  
Luis Marcelo Tavares de Carvalho ◽  
José Roberto Soares Scolforo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Image Segmentation ◽  
Artificial Neural Networks ◽  
Forest Inventory ◽  
Landsat Tm ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
Artificial Neural ◽  
Test Use

The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1) to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2) to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3) to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.

Detection of Eye Diseases using Image Processing and Artificial Neural Networks

2020 ◽  
pp. 15-20
Author(s):  
K. Sujatha ◽  
V. Srividhya ◽  
V. Karthikeyan ◽  
L. Madheshwaran ◽  
N. P. G. Bhavani
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Eye Diseases ◽  
Artificial Neural

Pig Weight Estimation Using Image Processing and Artificial Neural Networks

2019 ◽  
Vol 6 (4) ◽  
pp. 253-256
Author(s):  
Chanwit Kaewtapee ◽  
◽  
Choawit Rakangtong ◽  
Chaiyapoom Bunchasak
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Weight Estimation ◽  
Artificial Neural
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