ICTACT Journal on Image and Video Processing

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Plant Recognition System using Leaf Shape Features and Minimum Euclidean Distance


Affiliations
1 Department of Aerospace, Transport and Manufacturing, Cranfield University, United Kingdom
2 Department of Computer Science and Engineering, College of Engineering and Technology, International University of Business Agriculture and Technology, Bangladesh
     

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The study presents a plant recognition system that uses image and data processing techniques for recognition. A lot of research has been going on to identify plants by their leaves and one of the features that is used is the shape of the leaf but the accuracy is not high and therefore other features should also be considered to increase the accuracy. This system designed has three main steps which are image pre-processing, feature extraction and matching. Image pre-processing performs basic operations on the leaf image for segmentation which helps in making feature extraction easy. Seven (7) leaf features derived from geometric parameters of leaf shape were extracted from the pre-processed image and the simple principle of minimum Euclidean distance was used for finding the closest match to the input leaf image. The system used 10 species of leaves with a total of 50 leaf images from the flavia dataset for testing and obtained an accuracy above 90%. The algorithm is accurate and is easy to implement. However, it is slow and not tested on a large dataset. It is hoped that this proposed system will be exploited further and the speed will be improved and will also be able to give more information on the plant.

Keywords

Euclidean Distance, Feature Extraction, Image Pre-Processing, Leaf Classification, Specie Recognition, Image Segmentation.
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  • S.G. Wu, F.S. Bao, E.Y. Xu, Y. X. Wang, Y.F. Chang and Q.L. Xiang, “A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network”, Proceedings of IEEE International Symposium on Signal Processing and Information Technology, pp. 15-18, 2007.

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  • Plant Recognition System using Leaf Shape Features and Minimum Euclidean Distance

Abstract Views: 252  |  PDF Views: 1

Authors

Farhana Haque
Department of Aerospace, Transport and Manufacturing, Cranfield University, United Kingdom
Safwana Haque
Department of Computer Science and Engineering, College of Engineering and Technology, International University of Business Agriculture and Technology, Bangladesh

Abstract


The study presents a plant recognition system that uses image and data processing techniques for recognition. A lot of research has been going on to identify plants by their leaves and one of the features that is used is the shape of the leaf but the accuracy is not high and therefore other features should also be considered to increase the accuracy. This system designed has three main steps which are image pre-processing, feature extraction and matching. Image pre-processing performs basic operations on the leaf image for segmentation which helps in making feature extraction easy. Seven (7) leaf features derived from geometric parameters of leaf shape were extracted from the pre-processed image and the simple principle of minimum Euclidean distance was used for finding the closest match to the input leaf image. The system used 10 species of leaves with a total of 50 leaf images from the flavia dataset for testing and obtained an accuracy above 90%. The algorithm is accurate and is easy to implement. However, it is slow and not tested on a large dataset. It is hoped that this proposed system will be exploited further and the speed will be improved and will also be able to give more information on the plant.

Keywords


Euclidean Distance, Feature Extraction, Image Pre-Processing, Leaf Classification, Specie Recognition, Image Segmentation.

References