Grassland species characterization for plant family discrimination by image processing

Authors:
Mohamed Abadi;Anne-Sophie Capelle-Laizé;Majdi Khoudeir;Didier Combes;Serge Carré
Affiliations:
Université de Poitiers, XLIM-SIC, Chasseneuil;Université de Poitiers, XLIM-SIC, Chasseneuil;Université de Poitiers, XLIM-SIC, Chasseneuil;INRA Poitou-Charentes, Lusignan, France;INRA Poitou-Charentes, Lusignan, France
Venue:
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Year:
2010

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Abstract

Pasture species belonging to poaceae and fabaceae families constitute of essential elements to maintain natural and cultivated regions. Their balance and productivity are key factors for good functioning of the grassland ecosystems. The study is based on a process of image processing. First of all an individual signature is defined while considering geometric characteristics of each family. Then, this signature is used to discriminate between these families. Our approach focuses on the use of shape features in different situations. Specifically, the approach is based on cutting the representative leaves of each plant family. After cutting, we obtain leaves sections of different sizes and random geometry. Then, the shape features are calculated. Principal component analysis is used to select the most discriminatory features. The results will be used to optimize the acquisition conditions. We have a discrimination rate of more than 90% for the experiments carried out in a controlled environment. Experiments are being carried out to extend this study in natural environments.