DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-7, Issue-9 ( Sep, 2019 ) | |||||||||
Paper Title |
Machine Learning System using CNN Mobile Net Architecture for Image-Based Plant Diseases Detection | |||||||||
Author Name |
Htet Htet Aung, Sandor Markon | |||||||||
Affilition |
Kobe Institute of Computing (KIC) University, Kobe City, Japan | |||||||||
Pages |
20-23 | |||||||||
Abstract |
Agriculture is one of the most important sectors for many countries with the development of export the agricultural products. Plant diseases are common problem in the agricultural field which is also a major issue for farmers. The classification of plant diseases is very useful to manage the plant health situation during the cultivation time by using a good plant detection system. Machine learning is the most effective modern technique for image classification method. This paper introduces how to build Convolution Neural Network (CNN) machine learning model by using Mobile Net architecture to identify plant diseases. The purpose of this paper is to present a design of efficient models called Mobile Net Architecture with build the specification of image dataset and perform higher accuracy result compared to other popular Machine Learning models. Keywords - Machine Learning, Convolution Neural Network (CNN),Mobile Net Architecture, Tensor Flow, Image Processing, Beans, Plant Diseases. | |||||||||
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