Open AccessEnhancement of Plant Disease Detection Framework Using Cloud Computing and GPU Computing
Author(s) -
Parul Sharma*,
Yash paul Singh Berwal,
Wiqas Ghai
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a9541.109119
Subject(s) - cloud computing , computer science , bottleneck , image processing , distributed computing , parallel computing , artificial intelligence , image (mathematics) , operating system , embedded system
GPUs are very useful in high performance computing. With the emerging new trend of cloud environments with GPU instances are now gaining popularity in many real time applications. GPUs in a cloud environment still needs a long way to initiate various challenges in a cloud .The gap for making this as a shared resource in the cloud is still at preliminary level and still limited to many real life problems like plant disease detection at a grass root level. Timely information to farmers about diseases is still a great bottleneck for farmers. Due to this, farmers pour many rounds of pesticides to prevent their crops from diseases. But due to lack of proper ICT, farmers are not informed properly about their crop diseases which result in high loss to ecological balance and community people. Thus, to solve the problem of delayed updation about their crop diseases to farmers, GPU processing is used instead of Normal CPU processing on the cloud. This research paper focuses on the applications of GPU Computing within cloud and to study the performance of GPU image processing and Normal CPU image processing with respect to plant disease detection framework. This paper also addresses the problem of throttling in normal CPU when used for large datasets. GPU processors had shown a four-fold increase in performance as compared to normal datasets. GPU results shown 63 times faster as compared to normal CPU for analyzing 52,486 images of healthy and diseased leaf images. This include 16 plant types and 55 leaf diseases.