Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning
Stomata are the main medium of plants for the trade of water, regulate the gas exchange, and are responsible for the process of photosynthesis and transpiration. The stomata are surrounded by guard cells, which help to control the rate of transpiration by opening and closing the stomata. The stomata...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9938013 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850109586584895488 |
|---|---|
| author | Abdul Razzaq Sharaiz Shahid Muhammad Akram Muhammad Ashraf Shahid Iqbal Aamir Hussain M. Azam Zia Sulman Qadri Najia Saher Faisal Shahzad Ali Nawaz Shah Aziz-ur Rehman Sven-Erik Jacobsen |
| author_facet | Abdul Razzaq Sharaiz Shahid Muhammad Akram Muhammad Ashraf Shahid Iqbal Aamir Hussain M. Azam Zia Sulman Qadri Najia Saher Faisal Shahzad Ali Nawaz Shah Aziz-ur Rehman Sven-Erik Jacobsen |
| author_sort | Abdul Razzaq |
| collection | DOAJ |
| description | Stomata are the main medium of plants for the trade of water, regulate the gas exchange, and are responsible for the process of photosynthesis and transpiration. The stomata are surrounded by guard cells, which help to control the rate of transpiration by opening and closing the stomata. The stomata states (open and close) play a significant role in describing the plant’s health. Moreover, stomata counting is important for scientists to investigate the numbers of stomata that are open and those that are closed to measure their density and distribution on the surface of leaves through different sampling techniques. Although a few techniques for stomata counting have been proposed, these approaches do not identify and classify the stomata based on their states in leaves. In this research, we have developed an automatic system for stomata state identification and counting in quinoa leaf images through the transformed learning (neural network model Single Shot Detector) approach. In leaf imprint, the state of stomata has been determined by measuring the correlation between the area of stomata and the aperture of each detected stoma in the image. The stomata states have been classified through the Support Vector Machine (SVM) algorithm. The overall identification and classification accuracy of the proposed system are 98.6% and 97%, respectively, helping researchers to obtain accurate stomatal state information for leaves in an efficient and simple way. |
| format | Article |
| id | doaj-art-488f1ebd4a3c414aa34cc0d4dc91241c |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-488f1ebd4a3c414aa34cc0d4dc91241c2025-08-20T02:38:02ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/99380139938013Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep LearningAbdul Razzaq0Sharaiz Shahid1Muhammad Akram2Muhammad Ashraf3Shahid Iqbal4Aamir Hussain5M. Azam Zia6Sulman Qadri7Najia Saher8Faisal Shahzad9Ali Nawaz Shah10Aziz-ur Rehman11Sven-Erik Jacobsen12Department of Computer Science, MNS University of Agriculture, Multan, PakistanDepartment of Computer Science, MNS University of Agriculture, Multan, PakistanDepartment of Software Engineering, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, PakistanDepartment of Computer Engineering, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, PakistanDepartment of Agronomy, MNS, University of Agriculture, Multan, PakistanDepartment of Computer Science, MNS University of Agriculture, Multan, PakistanDepartment of Computer Science, University of Agriculture Faisalabad, Faisalabad, PakistanDepartment of Computer Science, MNS University of Agriculture, Multan, PakistanDepartment of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Computer Science, MNS University of Agriculture, Multan, PakistanDepartment of Plant and Environmental Sciences, University of Copenhagen, Copenhagen, DenmarkStomata are the main medium of plants for the trade of water, regulate the gas exchange, and are responsible for the process of photosynthesis and transpiration. The stomata are surrounded by guard cells, which help to control the rate of transpiration by opening and closing the stomata. The stomata states (open and close) play a significant role in describing the plant’s health. Moreover, stomata counting is important for scientists to investigate the numbers of stomata that are open and those that are closed to measure their density and distribution on the surface of leaves through different sampling techniques. Although a few techniques for stomata counting have been proposed, these approaches do not identify and classify the stomata based on their states in leaves. In this research, we have developed an automatic system for stomata state identification and counting in quinoa leaf images through the transformed learning (neural network model Single Shot Detector) approach. In leaf imprint, the state of stomata has been determined by measuring the correlation between the area of stomata and the aperture of each detected stoma in the image. The stomata states have been classified through the Support Vector Machine (SVM) algorithm. The overall identification and classification accuracy of the proposed system are 98.6% and 97%, respectively, helping researchers to obtain accurate stomatal state information for leaves in an efficient and simple way.http://dx.doi.org/10.1155/2021/9938013 |
| spellingShingle | Abdul Razzaq Sharaiz Shahid Muhammad Akram Muhammad Ashraf Shahid Iqbal Aamir Hussain M. Azam Zia Sulman Qadri Najia Saher Faisal Shahzad Ali Nawaz Shah Aziz-ur Rehman Sven-Erik Jacobsen Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning Complexity |
| title | Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning |
| title_full | Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning |
| title_fullStr | Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning |
| title_full_unstemmed | Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning |
| title_short | Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning |
| title_sort | stomatal state identification and classification in quinoa microscopic imprints through deep learning |
| url | http://dx.doi.org/10.1155/2021/9938013 |
| work_keys_str_mv | AT abdulrazzaq stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning AT sharaizshahid stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning AT muhammadakram stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning AT muhammadashraf stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning AT shahidiqbal stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning AT aamirhussain stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning AT mazamzia stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning AT sulmanqadri stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning AT najiasaher stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning AT faisalshahzad stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning AT alinawazshah stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning AT azizurrehman stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning AT svenerikjacobsen stomatalstateidentificationandclassificationinquinoamicroscopicimprintsthroughdeeplearning |