Deep learning-based detection and classification of water stress in lettuce through vision-based approach
Clicks: 2
ID: 286934
2023
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
0.3
/100
2 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #1,426 of 3,757 articles by views in Malay Journal
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
As the global population grows, the demand for food and water is increasing, necessitating the efficient use of limited agricultural resources. Water scarcity is a major issue that affects crop growth, yield, and quality. Traditional plant water stress detection methods are time-consuming, expensive, and impractical for large-scale applications. To address this, researchers are developing non-invasive vision-based techniques that detect plant water stress accurately using computer vision and machine learning. Deep learning, a subfield of machine learning, is effective in plant phenotyping. With this, the application of a vision system and deep learning in agriculture can help improve traditional agricultural practices and advance technology in the agriculture sector.
This study is focused on developing a vision-based approach to detect and classify water stress levels in green ice lettuce plants to aid in the irrigation management of the plants in a pot experiment setup. This system aims to help in the water management in the lettuce cultivation. No water stress, mild water stress, and severe water stress were simulated in the lettuce in the pot setup to gather the dataset of lettuce with varying water stress levels. The dataset was used to train a hybrid classification model wherein a pre-trained MobileNetV2 model was used to extract features from the images, and a Support Vector Machine algorithm was trained to classify the degree of water stress in lettuce. The vision-based classification model was implemented using a Raspberry Pi 4B.
Based on the results, the MobileNetV2-SVM model achieved an accuracy of 88.33%. The model’s training time was 238 seconds, and its inference time in the Raspberry Pi 4 was 0.5571 seconds. The implementation of the system to detect mild water stress in green ice lettuce resulted in an increase in crop yield and a reduction in water consumption. This implies that implementing a vision-based deep learning model to water stress in green ice lettuce can potentially boost water productivity, achieving more yield with less water.
| Reference Key |
persistent_1760660096_68f18a8026146
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Flores, Earl John C. |
| Journal | Malay Journal |
| Year | 2023 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.