Detection of plant leaf disease by generative adversarial and deep convolutional neural network
By: Deshpande, Rashmi
.
Contributor(s): Patidar, Hemant.
Publisher: USA Springer 2023Edition: Vol.104(5), Oct.Description: 1043-1052p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of the institution of engineers (India): Series BSummary: The majority of emerging countries' economic, social, and cultural development is highly dependent on the agricultural sector. Plant disease, however, might result in decreased crop yield and economic expansion. Numerous computer vision and artificial intelligence schemes have been put forth in the past for the automatic detection of plant leaf diseases, but their performance has fallen short due to problems with underprivileged feature representation, lower-order raw feature correlation, data imbalance, and a lack of generalization. In order to address the issue of data imbalance, this research describes the automatic diagnosis of plant leaf diseases using a generative adversarial network for data augmentation and a deep convolutional neural network to enhance feature representation and correlation. Ten tomato plant disease classes from the PlantVillage foliar disease database are the subject of extensive testing.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
|
School of Engineering & Technology Archieval Section | Not for loan | 2024-0114 |
The majority of emerging countries' economic, social, and cultural development is highly dependent on the agricultural sector. Plant disease, however, might result in decreased crop yield and economic expansion. Numerous computer vision and artificial intelligence schemes have been put forth in the past for the automatic detection of plant leaf diseases, but their performance has fallen short due to problems with underprivileged feature representation, lower-order raw feature correlation, data imbalance, and a lack of generalization. In order to address the issue of data imbalance, this research describes the automatic diagnosis of plant leaf diseases using a generative adversarial network for data augmentation and a deep convolutional neural network to enhance feature representation and correlation. Ten tomato plant disease classes from the PlantVillage foliar disease database are the subject of extensive testing.
Click on an image to view it in the image viewer
There are no comments for this item.