Detection of fungal diseases of plants from leaf images based on neural network technologies
The paper addresses the issue of automating the detection of fungal diseases in plants using digital images of their leaves. The spread of diseases among agricultural and horticultural crops causes significant economic losses worldwide, making the development of an effective and affordable solution...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
Institute Of Advanced Engineering And Science (IAES)
2024
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/29186/2/0248718082025133810.pdf http://eprints.utem.edu.my/id/eprint/29186/ https://ijece.iaescore.com/index.php/IJECE/article/view/34877 http://doi.org/10.11591/ijece.v14i5.pp5866-5873 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The paper addresses the issue of automating the detection of fungal diseases in plants using digital images of their leaves. The spread of diseases among agricultural and horticultural crops causes significant economic losses worldwide, making the development of an effective and affordable solution
to this problem highly valuable. Literature analysis suggests the viability of employing a convolutional neural network (CNN) to tackle this issue. The 'Fungus recognition' model was developed based on a custom CNN architecture using the TensorFlow library. The model underwent training
and testing on a publicly available dataset. Test results show that 'Fungus recognition' achieves a classification accuracy level of 90%, surpassing similar models considered. The developed model can be adapted for deployment on mobile computing devices, paving the way for its practical
implementation in agriculture and horticulture. |
|---|