Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses
The increased production of Reactive Oxygen Species (ROS) in plant leaf tissues is a hallmark of a plant’s reaction to various environmental stresses. This paper describes an automatic segmentation method for scanned images of cucurbits leaves stained to visualise ROS accumulation sites featured by...
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| Format: | Article |
| Language: | English |
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Sciendo
2012-09-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/v10006-012-0050-5 |
| _version_ | 1818727753096101888 |
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| author | Gocławski Jarosław Sekulska-Nalewajko Joanna Kuźniak Elżbieta |
| author_facet | Gocławski Jarosław Sekulska-Nalewajko Joanna Kuźniak Elżbieta |
| author_sort | Gocławski Jarosław |
| collection | DOAJ |
| description | The increased production of Reactive Oxygen Species (ROS) in plant leaf tissues is a hallmark of a plant’s reaction to various environmental stresses. This paper describes an automatic segmentation method for scanned images of cucurbits leaves stained to visualise ROS accumulation sites featured by specific colour hues and intensities. The leaves placed separately in the scanner view field on a colour background are extracted by thresholding in the RGB colour space, then cleaned from petioles to obtain a leaf blade mask. The second stage of the method consists in the classification of within mask pixels in a hue-saturation plane using two classes, determined by leaf regions with and without colour products of the ROS reaction. At this stage a two-layer, hybrid artificial neural network is applied with the first layer as a self-organising Kohonen type network and a linear perceptron output layer (counter propagation network type). The WTA-based, fast competitive learning of the first layer was improved to increase clustering reliability. Widrow–Hoff supervised training used at the output layer utilises manually labelled patterns prepared from training images. The generalisation ability of the network model has been verified by K-fold cross-validation. The method significantly accelerates the measurement of leaf regions containing the ROS reaction colour products and improves measurement accuracy. |
| first_indexed | 2024-12-17T22:19:06Z |
| format | Article |
| id | doaj.art-6a2e2bd694744294b3a3eddef408f66a |
| institution | Directory Open Access Journal |
| issn | 2083-8492 |
| language | English |
| last_indexed | 2024-12-17T22:19:06Z |
| publishDate | 2012-09-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Applied Mathematics and Computer Science |
| spelling | doaj.art-6a2e2bd694744294b3a3eddef408f66a2022-12-21T21:30:31ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922012-09-0122366968410.2478/v10006-012-0050-5Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stressesGocławski Jarosław0Sekulska-Nalewajko Joanna1Kuźniak Elżbieta2Institute of Applied Computer Science Łódź University of Technology, Stefanowskiego 18/22, 90-924 Łódź, PolandInstitute of Applied Computer Science Łódź University of Technology, Stefanowskiego 18/22, 90-924 Łódź, PolandDepartment of Plant Physiology and Biochemistry University of Łódź, Banacha 12/16, 90-237 Łódź, PolandThe increased production of Reactive Oxygen Species (ROS) in plant leaf tissues is a hallmark of a plant’s reaction to various environmental stresses. This paper describes an automatic segmentation method for scanned images of cucurbits leaves stained to visualise ROS accumulation sites featured by specific colour hues and intensities. The leaves placed separately in the scanner view field on a colour background are extracted by thresholding in the RGB colour space, then cleaned from petioles to obtain a leaf blade mask. The second stage of the method consists in the classification of within mask pixels in a hue-saturation plane using two classes, determined by leaf regions with and without colour products of the ROS reaction. At this stage a two-layer, hybrid artificial neural network is applied with the first layer as a self-organising Kohonen type network and a linear perceptron output layer (counter propagation network type). The WTA-based, fast competitive learning of the first layer was improved to increase clustering reliability. Widrow–Hoff supervised training used at the output layer utilises manually labelled patterns prepared from training images. The generalisation ability of the network model has been verified by K-fold cross-validation. The method significantly accelerates the measurement of leaf regions containing the ROS reaction colour products and improves measurement accuracy.https://doi.org/10.2478/v10006-012-0050-5image segmentationcolour spacemorphological processingimage thresholdingartificial neural networkwta learningwidrow–hoff learningcucurbita speciesplant stressros detection |
| spellingShingle | Gocławski Jarosław Sekulska-Nalewajko Joanna Kuźniak Elżbieta Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses International Journal of Applied Mathematics and Computer Science image segmentation colour space morphological processing image thresholding artificial neural network wta learning widrow–hoff learning cucurbita species plant stress ros detection |
| title | Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses |
| title_full | Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses |
| title_fullStr | Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses |
| title_full_unstemmed | Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses |
| title_short | Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses |
| title_sort | neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses |
| topic | image segmentation colour space morphological processing image thresholding artificial neural network wta learning widrow–hoff learning cucurbita species plant stress ros detection |
| url | https://doi.org/10.2478/v10006-012-0050-5 |
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