High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease
In some cases, image processing relies on a lot of training data to produce good and accurate models. It can be done to get an accurate model by augmenting the data, adjusting the darkness level of the image, and providing interference to the image. However, the more data that is trained, of course,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Padang
2022-06-01
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| Series: | JOIV: International Journal on Informatics Visualization |
| Subjects: | |
| Online Access: | https://joiv.org/index.php/joiv/article/view/793 |
| _version_ | 1811159847454900224 |
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| author | Evianita Dewi Fajrianti Afis Asryullah Pratama Jamal Abdul Nasyir Alfandino Rasyid Idris Winarno Sritrusta Sukaridhoto |
| author_facet | Evianita Dewi Fajrianti Afis Asryullah Pratama Jamal Abdul Nasyir Alfandino Rasyid Idris Winarno Sritrusta Sukaridhoto |
| author_sort | Evianita Dewi Fajrianti |
| collection | DOAJ |
| description | In some cases, image processing relies on a lot of training data to produce good and accurate models. It can be done to get an accurate model by augmenting the data, adjusting the darkness level of the image, and providing interference to the image. However, the more data that is trained, of course, requires high computational costs. One way that can be done is to add acceleration and parallel communication. This study discusses several scenarios of applying CUDA and MPI to train the 14.04 GB corn leaf disease dataset. The use of CUDA and MPI in the image pre-processing process. The results of the pre-processing image accuracy are 83.37%, while the precision value is 86.18%. In pre-processing using MPI, the load distribution process occurs on each slave, from loading the image to cutting the image to get the features carried out in parallel. The resulting features are combined with the master for linear regression. In the use of CPU and Hybrid without the addition of MPI there is a difference of 2 minutes. Meanwhile, in the usage between CPU MPI and GPU MPI there is a difference of 1 minute. This demonstrates that implementing accelerated and parallel communications can streamline the processing of data sets and save computational costs. In this case, the use of MPI and GPU positively influences the proposed system. |
| first_indexed | 2024-04-10T05:47:51Z |
| format | Article |
| id | doaj.art-b30fc26b6d0f4d2c89274847948d284f |
| institution | Directory Open Access Journal |
| issn | 2549-9610 2549-9904 |
| language | English |
| last_indexed | 2024-04-10T05:47:51Z |
| publishDate | 2022-06-01 |
| publisher | Politeknik Negeri Padang |
| record_format | Article |
| series | JOIV: International Journal on Informatics Visualization |
| spelling | doaj.art-b30fc26b6d0f4d2c89274847948d284f2023-03-05T10:28:41ZengPoliteknik Negeri PadangJOIV: International Journal on Informatics Visualization2549-96102549-99042022-06-016241141710.30630/joiv.6.2.793368High-Performance Computing on Agriculture: Analysis of Corn Leaf DiseaseEvianita Dewi Fajrianti0Afis Asryullah Pratama1Jamal Abdul Nasyir2Alfandino Rasyid3Idris Winarno4Sritrusta Sukaridhoto5Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, IndonesiaPoliteknik Elektronika Negeri Surabaya, Surabaya, 60111, IndonesiaPoliteknik Elektronika Negeri Surabaya, Surabaya, 60111, IndonesiaPoliteknik Elektronika Negeri Surabaya, Surabaya, 60111, IndonesiaPoliteknik Elektronika Negeri Surabaya, Surabaya, 60111, IndonesiaPoliteknik Elektronika Negeri Surabaya, Surabaya, 60111, IndonesiaIn some cases, image processing relies on a lot of training data to produce good and accurate models. It can be done to get an accurate model by augmenting the data, adjusting the darkness level of the image, and providing interference to the image. However, the more data that is trained, of course, requires high computational costs. One way that can be done is to add acceleration and parallel communication. This study discusses several scenarios of applying CUDA and MPI to train the 14.04 GB corn leaf disease dataset. The use of CUDA and MPI in the image pre-processing process. The results of the pre-processing image accuracy are 83.37%, while the precision value is 86.18%. In pre-processing using MPI, the load distribution process occurs on each slave, from loading the image to cutting the image to get the features carried out in parallel. The resulting features are combined with the master for linear regression. In the use of CPU and Hybrid without the addition of MPI there is a difference of 2 minutes. Meanwhile, in the usage between CPU MPI and GPU MPI there is a difference of 1 minute. This demonstrates that implementing accelerated and parallel communications can streamline the processing of data sets and save computational costs. In this case, the use of MPI and GPU positively influences the proposed system.https://joiv.org/index.php/joiv/article/view/793corn leaf diseaseimage analysisgpumpi. |
| spellingShingle | Evianita Dewi Fajrianti Afis Asryullah Pratama Jamal Abdul Nasyir Alfandino Rasyid Idris Winarno Sritrusta Sukaridhoto High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease JOIV: International Journal on Informatics Visualization corn leaf disease image analysis gpu mpi. |
| title | High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease |
| title_full | High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease |
| title_fullStr | High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease |
| title_full_unstemmed | High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease |
| title_short | High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease |
| title_sort | high performance computing on agriculture analysis of corn leaf disease |
| topic | corn leaf disease image analysis gpu mpi. |
| url | https://joiv.org/index.php/joiv/article/view/793 |
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