Automatic classification of legumes using leaf vein image features

• We develop an automatic procedure to classify legume species using scanned leaves.
• The method is based exclusively on the analysis of the leaf venation images.
• We analyze the advantages over the usage of cleared leaves.
• Different state-of-the-art classifiers are compared.
• The proposed method outperforms human expert classification.

In this paper, a procedure for segmenting and classifying scanned legume leaves based only on the analysis of their veins is proposed (leaf shape, size, texture and color are discarded). Three legume species are studied, namely soybean, red and white beans. The leaf images are acquired using a standard scanner. The segmentation is performed using the unconstrained hit-or-miss transform and adaptive thresholding. Several morphological features are computed on the segmented venation, and classified using four alternative classifiers, namely support vector machines (linear and Gaussian kernels), penalized discriminant analysis and random forests. The performance is compared to the one obtained with cleared leaves images, which require a more expensive, time consuming and delicate procedure of acquisition. The results are encouraging, showing that the proposed approach is an effective and more economic alternative solution which outperforms the manual expert's recognition.