Categorization of Plant Leaf Using CNN

Parvatikar, Suhasini; Parasar, Deepa

doi:10.1007/978-981-15-7421-4_7

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…The background image of a coin should be cropped to its maximum and more different shades of a coin should be added in the class to increase the accuracy classification rate. As a future work, the same study also can be applied in recognizing other objects other than coin such as handwriting character [22], car license plate [23], [24] and plant leaf [25]- [27].…”

Section: Resultsmentioning

confidence: 99%

Malaysia coin identification app using deep learning model

et al. 2023

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Most of the human work has been replaced by computers in recent years. With the rise of mobile technology and Internet access, recent developments in machine learning (ML) have designed many algorithms to solve diverse human problems. However, due to a lack of exposure to image processing, identification technology is still not widely employed in Malaysia. This paper outlines the steps involved in creating a mobile application for coin identification using ML. In the literature review, the history of the coins is studied in more depth and the features of already existing coin identification mobile applications are compared by their advantages and disadvantages. In addition, using the neural network model, the classification accuracy of successfully identified coins is recorded and disclosed. This study includes the limitations of the prototype mobile application and future improvements that could be added.

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Section: Resultsmentioning

confidence: 99%

Malaysia coin identification app using deep learning model

et al. 2023

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“…Neural network class encapsulates all the layers of the network. It has methods to train the network using mini-batch gradient descent, to compute the result of input, perform crossvalidation of the network, reset the weights of the network, and write the network to a file [26], [27].…”

Section: Convolutional Neural Network Methodsmentioning

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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“…This method could realize good performance in plant leaf identification. Some work (Parvatikar & Parasar, 2021) used a CNN model to extract leaf vein features of plant leaves and proved that increasing CNN depth could effectively promote the accuracy of plant leaf classification. Though the leaf identification network model based on deep learning enables automatic leaf feature extraction and produces results that are generalizable, this model has many parameters, which creates serious demands on computing resources.…”

Section: Literature Reviewmentioning

confidence: 99%

Rare and Endangered Plant Leaf Identification Method Based on Transfer Learning and Knowledge Distillation

Yang

et al. 2021

International Journal of Agricultural and Environmental Information Systems

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Considering the limited sample size of rare and endangered plant leaves and the issue that leaf identification is mainly conducted using mobile smart devices and other technology with low computing power, this paper proposes a rare and endangered plant leaf identification method based on transfer learning and knowledge distillation. Following the expansion of data sets containing rare and endangered plant leaves, the last fully connected layer was replaced with trained Alexnet, VGG16, GoogLeNet, and ResNet models to conduct transfer learning, and realized a relatively high success rate in identifying images of these species. Then, knowledge distillation was utilized to transfer Alexnet, VGG16, GoogLeNet, and ResNet models into a lightweight model. The experiment results indicate that, compared with other methods, the lightweight rare and endangered plant identification model trained with the methods described in this paper was not only more accurate but also less complex than its alternatives.

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Categorization of Plant Leaf Using CNN

Cited by 3 publications

References 2 publications

Malaysia coin identification app using deep learning model

Malaysia coin identification app using deep learning model

Plant leaf identification system using convolutional neural network

Rare and Endangered Plant Leaf Identification Method Based on Transfer Learning and Knowledge Distillation

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