One-hundred plant species leaves dataset. The dataset is derived from this paper: Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. Signal Processing, Pattern Recognition and Applications, in press. 2013.

(1)Sources:

(a) Original owners of colour Leaves Samples: James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.
The colour images are not included.
The Leaves were collected in the Royal Botanic Gardens, Kew, UK.
email: [email protected]

(b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell.

(2)Donor of the database:
Charles Mallah, [email protected]; James Cope, [email protected].

(3)Dataset Information:

The original data directory contains the binary images (masks) of the leaf samples (colour images not included). There are three features for each image: Shape, Margin and Texture. For each feature, a 64 element vector is given per leaf sample. These vectors are taken as a contiguous descriptor (for shape) or histograms (for texture and margin). So, there are three different files, one for each feature problem. Each row has a 64-element feature vector followed by the Class label. There is a total of 1600 samples with 16 samples per leaf class (100 classes), and no missing values.

‘data_Sha_64.txt’ -> prediction based on shape

‘data_Tex_64.txt’ -> prediction based on texture

‘data_Mar_64.txt’ -> prediction based on margin

(4)References:

[1]Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. Signal Processing, Pattern Recognition and Applications, in press.

[2]J. Cope, P. Remagnino, S. Barman, and P. Wilkin. Plant texture classification using gabor co-occurrences. Advances in Visual Computing, pages 699-677, 2010.

[3]T. Beghin, J. Cope, P. Remagnino, and S. Barman. Shape and texture based plant leaf classification. In: Advanced Concepts for Intelligent Vision Systems, pages 345-353. Springer, 2010.