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A Learning Robust and Discriminative Shape Descriptor for Plant Species Identification.

Chengzhuan Yang, Lincong Fang, Qian Yu

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|February 7, 2022

Summary

This summary is machine-generated.

This study introduces a novel High-Level Triangle Shape Descriptor (HTSD) for accurate plant identification from leaf images. The HTSD method demonstrates superior recognition accuracy compared to existing approaches.

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Area of Science:

Botany and Computer Vision
Artificial Intelligence applications in biological sciences

Background:

Accurate plant identification from leaf images is crucial in botany and AI.
Extracting robust, discriminative features for similarity measurement remains a challenge.

Purpose of the Study:

To develop an effective and robust shape descriptor for plant species identification using leaf images.
Introduce the High-Level Triangle Shape Descriptor (HTSD) for improved plant recognition.

Main Methods:

Extracting external leaf contours and internal salient point information.
Developing Contour Point based Triangle Features (CPTFs) and Salient Point Triangle Features (SPTFs).
Encoding local triangle features using Fisher vectors to create the HTSD and using Euclidean distance for similarity.

Main Results:

Extensive evaluation on public leaf datasets demonstrated successful application.
The proposed HTSD method achieved superior recognition accuracy.
Outperformed current state-of-the-art shape-based and deep-learning plant identification techniques.

Conclusions:

The High-Level Triangle Shape Descriptor (HTSD) offers a robust and accurate solution for plant identification.
This shape-based approach provides a competitive alternative to deep learning methods in plant recognition.