International Journal of Intelligent Information Systems
Volume 9, Issue 4, August 2020, Pages: 35-38
Received: Feb. 24, 2020;
Accepted: Mar. 17, 2020;
Published: Oct. 27, 2020
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Abhishek Agarwal, Computer Science Department, Christ University, Bangalore, India
Rohini Venkat, Computer Science Department, Christ University, Bangalore, India
Plants have a significant role in every corner, let it be for humans, animals, and the environment. They play a significant role in saving each other lives by providing each one with the necessities. For saving these plants, humans should be able to identify the plants in order to give proper treatment to the plants. The species of the plants can be easily identified by the venation of the leaves. This paper focuses on the Convolution Neural Networks (CNN) classification methodology, which helps to classify the leaves accurately. The work uses leaf images of apple, grape and tomatoes from the plant village dataset for getting the features and further classification of the leaves. The prediction of the leaves will be done by using the deep learning techniques in which the input layer will be the features extracted using the proposed algorithm. The proposed algorithm is based on Local Binary Pattern (LBP), which is a simple yet very efficient method to identify the pixels of the image by threshold in the neighborhood of each pixel and consider the result as a binary number. The proposed algorithm is efficient for its computational simplicity, which makes it possible to analyze images in challenging real-time settings in the field of image processing and computer vision.
Prediction of Leaves Using Convolutional Neural Network, International Journal of Intelligent Information Systems.
Vol. 9, No. 4,
2020, pp. 35-38.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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