Abstract
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.
Highlights
Abdul Razzaq,1 Sharaiz Shahid,1 Muhammad Akram,2 Muhammad Ashraf,3 Shahid Iqbal,4 Aamir Hussain,1 M
We have developed an automatic system for stomata state identification and counting in quinoa leaf images through the transformed learning approach
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. e stomata states have been classified through the Support Vector Machine (SVM) algorithm. e 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
Summary
Abdul Razzaq ,1 Sharaiz Shahid, Muhammad Akram, Muhammad Ashraf, Shahid Iqbal ,4 Aamir Hussain, M. 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. 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. We propose an automatic system for the identification of stomatal state (open and close) and stomata counting in microscopic imprints using a deep convolutional neural network, Single Shot Detection (SSD-MobileNet V2). Is method was used for the detection, counting, and state identification of stomata in microscopic imprints of quinoa crop leaf. Stomatal states were classified through a Support Vector Machine (SVM), which is a supervised machine learning model that uses a classification algorithm for two-group classification problems
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