PLANTNET: A DEEP LEARNING MODEL FOR EARLY DETECTION OF PLANT DISEASES
Plant leaf disease detection in high-value crops is an important problem for farmers and the agricultural industry, often resulting in significant crop losses and economic losses. This paper presents a deep learning model, PlantNET, for early identification of plant leaf infections based on Convolut...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
XLESCIENCE
2024-12-01
|
| Series: | International Journal of Advances in Signal and Image Sciences |
| Subjects: | |
| Online Access: | https://xlescience.org/index.php/IJASIS/article/view/177 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Plant leaf disease detection in high-value crops is an important problem for farmers and the agricultural industry, often resulting in significant crop losses and economic losses. This paper presents a deep learning model, PlantNET, for early identification of plant leaf infections based on Convolutional Neural Networks (CNNs) trained on a large collection of leaf images in the PlantVillage database, including both healthy and infected samples from several crops. PlantNET is constructed efficiently to capture the characteristics associated with plant leaf infections and is optimized to provide better accuracy. The PlantNet’s performance is computed regarding accuracy, precision, and recall measures. It enables quick diagnosis of infections, allowing for quick intervention solutions to minimize crop loss and the requirement for chemical treatments. The usefulness of PlantNet in agricultural applications emphasizes its potential to improve farming sustainability. The results highlight the need to use modern technology in precision agriculture to protect crop health and boost farmer profitability. |
|---|---|
| ISSN: | 2457-0370 |