<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses

Institute of Applied Computer Science Łódź University of Technology, Stefanowskiego 18/22, 90-924 Łódź, Poland

Department of Plant Physiology and Biochemistry University of Łódź, Banacha 12/16, 90-237 Łódź, Poland

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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...

Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses

Data publikacji: 28 wrz 2012

Zakres stron: 669 - 684

DOI: https://doi.org/10.2478/v10006-012-0050-5

Słowa kluczowe
image segmentation, colour space, morphological processing, image thresholding, artificial neural network, WTA learning, Widrow–Hoff learning, Cucurbita species, plant stress, ROS detection

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses

Data publikacji: 28 wrz 2012

Zakres stron: 669 - 684

DOI: https://doi.org/10.2478/v10006-012-0050-5

Słowa kluczoweimage segmentation, colour space, morphological processing, image thresholding, artificial neural network, WTA learning, Widrow–Hoff learning, Cucurbita species, plant stress, ROS detection

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
image segmentation, colour space, morphological processing, image thresholding, artificial neural network, WTA learning, Widrow–Hoff learning, Cucurbita species, plant stress, ROS detection