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An automatic segmentation method for scanned images of wheat root systems with dark discolourations

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Gocławski J., Sekulska-Nalewajko J., Gajewska E., Wielanek M.

International Journal of Applied Mathematics and Computer Science

2009

tom 19

nr 4

679-689

The analysis of plant root system images plays an important role in the diagnosis of plant health state, the detection of possible diseases and growth distortions. This paper describes an initial stage of automatic analysis-the segmentation method for scanned images of Ni-treated wheat roots from hydroponic culture. The main roots of a wheat fibrous system are placed separately in the scanner view area on a high chroma background (blue or red). The first stage of the method includes the transformation of a scanned RGB image into the HCI (Hue-Chroma-Intensity) colour space and then local thresholding of the chroma component to extract a binary root image. Possible chromatic discolourations, different from background colour, are added to the roots from blue or red chroma subcomponent images after thresholding. At the second stage, dark discolourations are extracted by local fuzzy c-means clustering of an HCI intensity image within the binary root mask. Fuzzy clustering is applied in local windows around the series of sample points on roots medial axes (skeleton). The performance of the proposed method is compared with hand-labelled segmentation for a series of several root systems.

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

100%

Gocławski J., Sekulska-Nalewajko J., Kuźniak E.

International Journal of Applied Mathematics and Computer Science

2012

tom 22

nr 3

669-684

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.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

2 Gocławski J.

2 Sekulska-Nalewajko J.

1 Gajewska E.

1 Kuźniak E.

1 Wielanek M.

1 2012

1 2009

Wyniki wyszukiwania

An automatic segmentation method for scanned images of wheat root systems with dark discolourations

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