How deep learning extracts and learns leaf features for plant classification

By Sue Han Lee, Chee Seng Chan, Simon Mayo, Paolo Remagnino. Pattern Recognition, 2017

525 citations recorded by the source corpus.

Abstract Plant identification systems developed by computer vision researchers have helped botanists to recognize and identify unknown plant species more rapidly. Hitherto, numerous studies have focused on procedures or algorithms that maximize the use of leaf databases for plant predictive modeling, but this results in leaf features which are liable to change with different leaf data and feature extraction techniques. In this paper, we learn useful leaf features directly from the raw representations of input data using Convolutional Neural Networks (CNN), and gain intuition of the chosen features based on a Deconvolutional Network (DN) approach. We report somewhat unexpected results: (1) different orders of venation are the best representative features compared to those of outline shape, and (2) we observe multi-level representation in leaf data, demonstrating the hierarchical transformation of features from lower-level to higher-level abstraction, corresponding to species classes. We show that these findings fit with the hierarchical botanical definitions of leaf characters. Through these findings, we gained insights into the design of new hybrid feature extraction models which are able to further improve the discriminative power of plant classification systems. The source code and models are available at: https://github.com/cs-chan/Deep-Plant .

Subfields: Ecology, Pharmacology, Plant Science

View Paper: https://www.semanticscholar.org/paper/e10b86506d54280caff626ea83d6ed904ab71bb5

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Citations in the corpus

Tree Species Identification from Bark Images Using Convolutional Neural Networks (2018, methodology): “Less related to the work described herein, work on leaf classification by [21] extracted”
Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach (2021, background): “Deep learning (DL) consists of different convolutional layers that represent learning features from the data [19,20].”
A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning (2019, methodology): “• Plant Classification Lee et al. [28] presented a new method in which deep learning methods are used for the plant classification.”
Getting to Know Low-light Images with The Exclusively Dark Dataset (2018, background): “Nevertheless, many works had since visualized high dimensional data and features ([30], [51], [52], [53], [31]) to understand and ﬁnd out what the machines “see”.”
Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm (2018, methodology): “[3] introduce a hybrid model to extract contextual information of leaf features using CNN and Deconvolutional Networks (DN).”
Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective (2018, unknown): “In the last few years, many CNN-based approaches have been developed for the taxonomic classification of plants [52, 53].”
Automated plant species identification—Trends and future directions (2018, methodology): “’s [53, 54] leaf classifier that uses the AlexNet architecture pretrained on the ILSVRC2012 dataset and reached an average accuracy of 99.”