Deep-plant: Plant identification with convolutional neural networks

Lee, Sue Han; Chan, Chee Seng; Wilkin, Paul; Remagnino, Paolo

doi:10.1109/icip.2015.7350839

Cited by 360 publications

(192 citation statements)

References 18 publications

Supporting

Mentioning

171

Contrasting

Unclassified

Order By: Relevance

“…With this background, we provide in this paper a solution for the quantification of prominent leaf features. A preliminary version of this work was presented earlier [31]. The present work adds to the initial version in significant ways.…”

Section: Related Studiesmentioning

confidence: 98%

“…In this study, based on our discovery that CNN trained on different input data formats provides variants of contextual features of leaf, we design a new hybrid global-local feature extraction model for leaf data based on CNN approach. Instead of relying on either whole leaf data [31,45,46,47] or solely venation [30,31] for species classification, we propose to combine information from two CNN networks, one global network trained upon the whole leaf data and another local network trained upon its corresponding leaf patches. We integrate them via different feature fusion strategies as illustrated in Fig.…”

Section: Hybrid Global-local Leaf Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

How deep learning extracts and learns leaf features for plant classification

Lee

Chan

Mayo

et al. 2017

Pattern Recognition

Self Cite

435

172

View full text Add to dashboard Cite

Highlights• A deep learning approach to quantify discriminatory leaf is proposed.• Shape is not a dominant feature for leaf but rather the different orders of venation.• Deep learning reveals transformation of leaf features from general to specific types.• Findings archived fit with the hierarchical botanical definitions of leaf characters• Features learned using deep learning can improve plant recognition performance. AbstractPlant 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.

show abstract

Section: Related Studiesmentioning

confidence: 98%

Section: Hybrid Global-local Leaf Feature Extractionmentioning

confidence: 99%

How deep learning extracts and learns leaf features for plant classification

Lee

Chan

Mayo

et al. 2017

Pattern Recognition

Self Cite

435

172

View full text Add to dashboard Cite

show abstract

“…Lee et al (2015) presented a leaf-based plant classification using CNNs to automatically learn the discriminative features. Grinblat et al (2016) employed a 3-layer CNN for assessing the classification performance on three different legume species and they emphasised the relevance of vein patterns.…”

Section: Introductionmentioning

confidence: 99%

Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition

Pawara

Okafor

Surinta³

et al. 2017

Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods

View full text Add to dashboard Cite

Abstract:The use of machine learning and computer vision methods for recognizing different plants from images has attracted lots of attention from the community. This paper aims at comparing local feature descriptors and bags of visual words with different classifiers to deep convolutional neural networks (CNNs) on three plant datasets; AgrilPlant, LeafSnap, and Folio. To achieve this, we study the use of both scratch and fine-tuned versions of the GoogleNet and the AlexNet architectures and compare them to a local feature descriptor with k-nearest neighbors and the bag of visual words with the histogram of oriented gradients combined with either support vector machines and multi-layer perceptrons. The results shows that the deep CNN methods outperform the hand-crafted features. The CNN techniques can also learn well on a relatively small dataset, Folio.

show abstract

“…Several researchers use CNNs to identify plant images. Lee et al [5] presented system that utilizes CNN to automatically learn discriminative features from leaf images. Reyes et al [6] fine-tuned a CNN model for plants identification achieving a great success.…”

Section: Introductionmentioning

confidence: 99%

“…Reyes et al [6] fine-tuned a CNN model for plants identification achieving a great success. References [5,6] are both based on the CNN where the architecture was firstly proposed by Krizhevsky [7]. This paper presents a deep learning model for flower grading.…”

Section: Introductionmentioning

confidence: 99%

Multi-Input Convolutional Neural Network for Flower Grading

Sun

Zhu

Wang

et al. 2017

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images. A multi-input convolutional neural network is designed for large scale flower grading. Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation. Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading. Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity. Majority of misclassification is derived from the medium class. The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%.

show abstract

Deep-plant: Plant identification with convolutional neural networks

Cited by 360 publications

References 18 publications

How deep learning extracts and learns leaf features for plant classification

How deep learning extracts and learns leaf features for plant classification

Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition

Multi-Input Convolutional Neural Network for Flower Grading

Contact Info

Product

Resources

About