High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease

Evianita Dewi Fajrianti - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia
Afis Asryullah Pratama - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia
Jamal Abdul Nasyir - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia
Alfandino Rasyid - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia
Idris Winarno - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia
Sritrusta Sukaridhoto - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia


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DOI: http://dx.doi.org/10.30630/joiv.6.2.793

Abstract


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.

Keywords


Corn leaf disease; image analysis; GPU; MPI.

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References


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