- Tytuł:
- Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses
- Autorzy:
-
Gocławski, J.
Sekulska-Nalewajko, J.
Kuźniak, E. - Powiązania:
- https://bibliotekanauki.pl/articles/330961.pdf
- Data publikacji:
- 2012
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
segmentacja obrazu
przestrzeń koloru
przetwarzanie morfologiczne
progowanie obrazu
sztuczna sieć neuronowa
ochrona roślin
image segmentation
colour space
morphological processing
image thresholding
artificial neural network
WTA learning
Widrow-Hoff learning
Cucurbita species
plant stress
ROS detection - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 669-684
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł