Mobile plant leaf identification using smart-phones

Wang, Bin; Brown, Douglas; Gao, Yongsheng

doi:10.1109/icip.2013.6738910

Cited by 19 publications

(9 citation statements)

References 13 publications

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“…However, in remote areas where plant identification applications are likely to be most useful, an Internet connection may be unavailable or unreliable. The alternative approach is to use efficient algorithms that run directly on the device without the need for a network connection or a support server but with potential limitations in the classification performance [39,40].…”

Section: Mobile Applicationmentioning

confidence: 99%

“…In another Android application in Java, classification can either be done on the server for more computationally expensive algorithms or offline on the device [40]. The online mode involved sending only the feature vector to the server rather than the actual image.…”

Section: Mobile Applicationsmentioning

confidence: 99%

“…In our implementation, we provide interfaces to both an online and offline tree image database. The feature extraction could alternatively be performed at the server for more computationally expensive algorithms [40].…”

Section: Mobile Implementationmentioning

confidence: 99%

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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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Section: Mobile Applicationmentioning

confidence: 99%

Section: Mobile Applicationsmentioning

confidence: 99%

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

Chung

Chou²,

2021

IJERPH

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“…Plant recognition for plant species management was significant in agriculture; thus, we firmly believe, botanists can use this system in honour of leaf for medicinal [13]. Most of the plants' leaf has specific characteristics, and each of characteristics can be used for plants identification.…”

Section: Introductionmentioning

confidence: 99%

Plant leaf identification system using convolutional neural network

Taslim¹,

Saon

Mahamad

et al. 2021

Bulletin EEI

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This paper proposes a leaf identification system using convolutional neural network (CNN). This proposed system can identify five types of local Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By using CNN from deep learning, the network is trained from the database that acquired from leaf images captured by mobile phone for image classification. ResNet-50 was the architecture has been used for neural networks image classification and training the network for leaf identification. The recognition of photographs leaves requested several numbers of steps, starting with image pre-processing, feature extraction, plant identification, matching and testing, and finally extracting the results achieved in MATLAB. Testing sets of the system consists of 3 types of images which were white background, and noise added and random background images. Finally, interfaces for the leaf identification system have developed as the end software product using MATLAB app designer. As a result, the accuracy achieved for each training sets on five leaf classes are recorded above 98%, thus recognition process was successfully implemented.

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“…Leaves have various characteristics that can be used for recognition such as shape, color and texture. Shape feature has been used in [6] where the leaf's shape contour, the convexity and concavity properties of the arches are measured. Hu invariant moments that represent the shape of the leaf have been utilized in [7].…”

Section: Introductionmentioning

confidence: 99%

Leaf Recognition using Texture Features for Herbal Plant Identification

İbrahim

Sabri

Mangshor

2018

IJEECS

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This research investigates the application of texture features for leaf recognition for herbal plant identification. Malaysia is rich with herbal plants but not many people can identify them and know about their uses. Preservation of the knowledge of these herb plants is important since it enables the general public to gain useful knowledge which they can apply whenever necessary. Leaf image is chosen for plant recognition since it is available and visible all the time. Unlike flowers that are not always available or roots that are not visible and not easy to obtain, leaf is the most abundant type of data available in botanical reference collections. A comparative study has been conducted among three popular texture features that are Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Speeded-Up Robust Features (SURF) with multiclass Support Vector Machine (SVM) classifier. A new leaf dataset has been constructed from ten different herb plants. Experimental results using the new constructed dataset and Flavia, an existing dataset, indicate that HOG and LBP produce similar leaf recognition performance and they are better than SURF.

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Mobile plant leaf identification using smart-phones

Cited by 19 publications

References 13 publications

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

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

Plant leaf identification system using convolutional neural network

Leaf Recognition using Texture Features for Herbal Plant Identification

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