ApLeafis: An Android-Based Plant Leaf Identification System

Ma, Lin-Hai; Zhao, Zhong-Qiu; Wang, Jing

doi:10.1007/978-3-642-39479-9_13

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…Apart from studying the whole shape contour of the leaf, some studies [9,23] analysed leaf margins for species classification. There are also some groups of researchers who are incorporating plant identification into mobile computing technology such as Leafsnap [7] and Apleafis [24].…”

Section: Related Studiesmentioning

confidence: 99%

How deep learning extracts and learns leaf features for plant classification

Lee

Chan

Mayo

et al. 2017

Pattern Recognition

434

172

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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.

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Section: Related Studiesmentioning

confidence: 99%

How deep learning extracts and learns leaf features for plant classification

Lee

Chan

Mayo

et al. 2017

Pattern Recognition

434

172

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“…ApLeafis [58] is an Android-based plant leaf identification system in JAVA based on content-based image retrieval technique. An isolated leaf needs to be properly shot on a light, plain, and uniform background.…”

Section: Mobile Applicationsmentioning

confidence: 99%

Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition

Chung

Chou²,

2021

IJERPH

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Identifying plants is not only the job of professionals, but also useful or essential for the plant lover and the general public. Although deep learning approaches for plant recognition are promising, driven by the success of convolutional neural networks (CNN), their performances are still far from the requirements of an in-field scenario. First, we propose a central attention concept that helps focus on the target instead of backgrounds in the image for tree species recognition. It could prevent model training from confused vision by establishing a dual path CNN deep learning framework, in which the central attention model combined with the CNN model based on InceptionV3 were employed to automatically extract the features. These two models were then learned together with a shared classification layer. Experimental results assessed the effectiveness of our proposed approach which outperformed each uni-path alone, and existing methods in the whole plant recognition system. Additionally, we created our own tree image database where each photo contained a wealth of information on the entire tree instead of an individual plant organ. Lastly, we developed a prototype system of an online/offline available tree species identification working on a consumer mobile platform that can identify the tree species not only by image recognition, but also detection and classification in real-time remotely.

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“…Therefore, there is a need for protecting plants and classifying them to different species. For this purpose, the identification techniques have become a hot area of research [1]. Manual plant identification requires extensive knowledge and it utilizes complex terminology in a way that even a professional botanist needs to spend much time in a field to achieve plant identification [2].…”

Section: Introductionmentioning

confidence: 99%

Plant Identification: Two Dimensional-Based Vs. One Dimensional-Based Feature Extraction Methods

Gaber

Tharwat

Snášel

et al. 2015

Advances in Intelligent Systems and Computing

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In this paper, a plant identification approach using 2D digital leaves images is proposed. The approach made use of two methods of features extraction (one-dimensional (1D) and two-dimensional (2D) techniques) and the Bagging classifier. For the 1D-based method, PCA and LDA techniques were applied, while 2D-PCA and 2D-LDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner, was used. The proposed approach, with its four feature extraction techniques, was tested using Flavia dataset which consists of 1907 colored leaves images. The experimental results showed that the accuracy and the performance of our approach, with the 2D-PCA and 2D-LDA, was much better than using the PCA and LDA. Furthermore, it was proven that the 2D-LDA-based method gave the best plant identification accuracy and increasing the weak learners of the Bagging classifier leaded to a better accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup.

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ApLeafis: An Android-Based Plant Leaf Identification System

Cited by 10 publications

References 6 publications

How deep learning extracts and learns leaf features for plant classification

How deep learning extracts and learns leaf features for plant classification

Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition

Plant Identification: Two Dimensional-Based Vs. One Dimensional-Based Feature Extraction Methods

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