Two-View Fine-grained Classification of Plant Species

by   Voncarlos M. Araujo, et al.

Automatic plant classification is a challenging problem due to the wide biodiversity of the existing plant species in a fine-grained scenario. Powerful deep learning architectures have been used to improve the classification performance in such a fine-grained problem, but usually building models that are highly dependent on a large training dataset and which are not scalable. In this paper, we propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. It uses the botanical taxonomy as a basis for a coarse-to-fine strategy applied to identify the plant genus and species. The two-view representation provides complementary global and local features of leaf images. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species. The experimental results on two challenging fine-grained datasets of leaf images (i.e. LifeCLEF 2015 and LeafSnap) have shown the effectiveness of the proposed method, which achieved recognition accuracy of 0.87 and 0.96 respectively.


page 4

page 7

page 8

page 9

page 10


Recognition of Plant Species using Deep Convolutional Feature Extraction

There are more than 391,000 plant species currently known to global scie...

CoCoNet: A Collaborative Convolutional Network

We present an end-to-end CNN architecture for fine-grained visual recogn...

From Species to Cultivar: Soybean Cultivar Recognition using Multiscale Sliding Chord Matching of Leaf Images

Leaf image recognition techniques have been actively researched for plan...

Multi-resolution Outlier Pooling for Sorghum Classification

Automated high throughput plant phenotyping involves leveraging sensors,...

Comparison between transformers and convolutional models for fine-grained classification of insects

Fine-grained classification is challenging due to the difficulty of find...

Plant Species Recognition with Optimized 3D Polynomial Neural Networks and Variably Overlapping Time-Coherent Sliding Window

Recently, the EAGL-I system was developed to rapidly create massive labe...

Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species Annotations

Insects are a crucial part of our ecosystem. Sadly, in the past few deca...

Please sign up or login with your details

Forgot password? Click here to reset