Shape-only Features for Plant Leaf Identification

2018/11/20 by Charlie Hewitt, Hewitt, Charlie, Marwa Mahmoud +1
Agricultural and Biological Sciences · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Leaf Properties and Growth Measurement #Remote Sensing in Agriculture #Smart Agriculture and AI

paper · pdf · doi:10.48550/arxiv.1811.08398

openalex publication_date 2018/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents a novel feature set for shape-only leaf identification motivated by real-world, mobile deployment. The feature set includes basic shape features, as well as signal features extracted from local area integral invariants (LAIIs), similar to curvature maps, at multiple scales. The proposed methodology is evaluated on a number of publicly available leaf datasets with comparable results to existing methods which make use of colour and texture features in addition to shape. Over 90% classification accuracy is achieved on most datasets, with top-four accuracy for these datasets reaching over 98%. Rotation and scale invariance of the proposed features are demonstrated, along with an evaluation of the generalisability of the approach for generic shape matching.

Citations

• Leaf Classification Using Shape, Color, and Texture Features
• Treelogy: A Novel Tree Classifier Utilizing Deep and Hand-crafted Representations

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