Early Prediction of Plant Disease Using AI Enabled IOT
- Title
- Early Prediction of Plant Disease Using AI Enabled IOT
- Creator
- Vijayalakshmi S.; Balakrishnan G.; Lakshmi S.N.
- Description
- India is an industrialized country, and about 70% of the residents rely on agriculture. Leaves are damaged by chemicals, and climates issues. An unknown illness is found on plants leads to the lowering of quality of produced. Internet of Things is a practice of reinventing the wheel agriculture by enabling farmers to tackle the problems in the industry with practical farming techniques. IoT helps to inform knowledge about factors like weather, and moisture condition. We proposed IoT, ML, and image processing based method to identify the infection. IOT enabled camera to capture the image then required region of interest is extracted. After ROI extraction, image is enhanced to remove the unwanted details form the image and to improve image quality. We compute image features. At the end we do the classification which is a twostep process training and testing and done by SVM. Our proposed method gives 92% accuracy. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes in Networks and Systems, Vol-290, pp. 303-309.
- Date
- 2021-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Artificial neural network; Image features; Internet of Thing; Plant disease; Region of interest; Support vector machine
- Coverage
- Vijayalakshmi S., Christ University, Bangalore, India; Balakrishnan G., Fatima Michael College of Engineering and Technology, Madurai, India; Lakshmi S.N., Fatima Michael College of Engineering and Technology, Madurai, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981164485-6
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Vijayalakshmi S.; Balakrishnan G.; Lakshmi S.N., “Early Prediction of Plant Disease Using AI Enabled IOT,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/20593.