Leaf Disease Identification in Rice Plants Using CNN Model
- Title
- Leaf Disease Identification in Rice Plants Using CNN Model
- Creator
- Reddy A.S.; Thomas J.
- Description
- Rice is a staple food crop for more than 10 countries. High consumption of rice demands better yield of crop. Fungal, bacterial and viral are different classes of diseases damaging rice crops which results in low and bad yield as per quality and quantity of the crop. Some of the most common diseases affecting plants are fungal blast, fungal brown spot, fungal sheath blight, bacterial blight and viral tungro. The deep learning CNN model with ResNet50V2 architecture was used in this paper to identify disease on the paddy leaves. Mobile application proposed in this paper will help farmers to detect disease on the leaves during their regular visit. Images were captured using this application. The captured images were tested using the trained deep learning model embedded with mobile application. This model predicts and displays input images along with the probabilities compared to each disease. The mobile application also provides necessary remedies for the identified disease with the help of hyperlink available in mobile application. The achieved probability that the model can truly classify the input image in this project was 97.67%, and the obtained validation accuracy was 98.86%. A solution with which farmers can identify diseases in rice leaves and take necessary actions for better crop yield has been demonstrated in this paper. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 17-31.
- Date
- 2022-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- CNN; Deep learning; Mobile application; ResNet50V2
- Coverage
- Reddy A.S., Christ (Deemed-to-be-University), Karnataka, Bangalore, India; Thomas J., Christ (Deemed-to-be-University), Karnataka, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23674512
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Reddy A.S.; Thomas J., “Leaf Disease Identification in Rice Plants Using CNN Model,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 28, 2025, https://archives.christuniversity.in/items/show/18655.