The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Smart Farming: An Approach for Disease Detection Implementing IoT and Image Processing
|
|
Author(s): Hui Pang (Hebei University of Architecture, China), Zheng Zheng (Tangshan Normal University, China), Tongmiao Zhen (College of Information Engineering, Hebei University of Architecture, China)and Ashutosh Sharma (Institute of Computer Technology and Information Security, Southern Federal University, Russia)
Copyright: 2021
Volume: 12
Issue: 1
Pages: 13
Source title:
International Journal of Agricultural and Environmental Information Systems (IJAEIS)
Editor(s)-in-Chief: Frederic Andres (National Institute of Informatics, Japan), Chutiporn Anutariya (Asian Institute of Technology, Thailand), Teeradaj Racharak (Japan Advanced Institute of Science and Technology, Japan)and Watanee Jearanaiwongkul (National institute of Informatics, Japan)
DOI: 10.4018/IJAEIS.20210101.oa4
Purchase
|
Abstract
With the increasing demand on smart agriculture, the effective growth of a plant and increase its productivity are essential. To increase the yield and productivity, monitoring of a plant during its growth till its harvesting is a foremost requirement. In this article, an image processing-based algorithm is developed for the detection and monitoring of diseases in fruits from plantation to harvesting. The concept of artificial neural network is employed to achieve this task. Four diseases of tomato crop have been selected for the study. The proposed system uses two image databases. The first database is used for training of already infected images and second for the implementation of other query images. The weight adjustment for the training database is carried out by concept of back propagation. The experimental results present the classification and mapping of images to their respective categories. The images are categorized as color, texture, and morphology. The morphology gives 93% correct results which is more than the other two features. The designed algorithm is very effective in detecting the spread of disease. The practical implementation of the algorithm has been done using MATLAB.
Related Content
|
Vincent Soulignac, François Pinet, Mathilde Bodelet, Hélène Gross.
© 2023.
28 pages.
|
|
Haiying Liu, Yongcai Lai, Zhenhua Xu, Zhonliang Yang, Yanmin Yu, Ping Yan.
© 2023.
12 pages.
|
|
Ren Wang.
© 2023.
14 pages.
|
|
Daidyi Wang, Fengsong Zhang.
© 2022.
15 pages.
|
|
Takahiro Kawamura, Tetsuo Katsuragi, Akio Kobayashi, Motoko Inatomi, Masataka Oshiro, Hisashi Eguchi.
© 2022.
19 pages.
|
|
Cédric Baudrit, Patrice Buche, Nadine Leconte, Christophe Fernandez, Maëllis Belna, Geneviève Gésan-Guiziou.
© 2022.
22 pages.
|
|
Jingfa Wang, Huishi Du.
© 2022.
11 pages.
|
|
|