Plant Disease Detection Using Sequential Convolutional Neural Network

Abstract

The main warning in the area of food preservation and care is on topmost are crop diseases. It has been recognized speedily, but it is not as easy as in any area of the world because no required framework exists. Both the healthy and diseased plant leaves were gathered and collected under the condition and circumstances. For this purpose, a public set of information was used. It was 20,639 images of plants that were infected and healthy. In order to recognize three different crops and 12 diseases, a sequential convolutional neural network from Keras was trained and applied. The perfection and exactness was 98.18 % onset of information of the above trained mentioned model using CNN . It has also indicated the probability and possibility of this strategy and procedure. The over-fitting occurs and neutralizes by putting the dropout value to 0.25.

Publication DOI: https://doi.org/10.4018/ijdst.303672
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © The Authors. This AAM is licensed under a CC BY 4.0 license
Uncontrolled Keywords: Computer Networks and Communications,Hardware and Architecture
Publication ISSN: 1947-3540
Last Modified: 13 Jun 2024 07:21
Date Deposited: 26 Jul 2022 11:43
Full Text Link:
Related URLs: https://www.igi ... /article/303672 (Publisher URL)
PURE Output Type: Article
Published Date: 2022-01-01
Accepted Date: 2022-01-01
Authors: Tripathi, Anshul
Chourasia, Uday
Dixit, Priyanka
Chang, Victor (ORCID Profile 0000-0002-8012-5852)

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License: Creative Commons Attribution

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