Plant leaf identification system using convolutional neural network

Taslim, Amiruzzaki; Saon, Sharifah; Mahamad, Abd Kadir; Muladi, Muladi; Hidayat, Wahyu

doi:10.11591/eei.v10i6.2332

Cited by 6 publications

(6 citation statements)

References 21 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Most of the human work has been replaced by computers in recent years.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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“…ResNet is a kind of DNN based on ResNet that is made up of residual blocks [18], [23], [59] [61]. These blocks have skip or shortcut connections that let identity mappings get through weight layers.…”

Section: Resnetmentioning

confidence: 99%

Deep neural networks optimization for resource-constrained environments: techniques and models

Careem,

Md Johar,

Khatibi

2024

IJEECS

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This paper aims to present a comprehensive review of advanced techniques and models with a specific focus on deep neural network (DNN) for resource-constrained environments (RCE). The paper contributes by highlighting the RCE devices, analyzing challenges, reviewing a broad range of optimization techniques and DNN models, and offering a comparative assessment. The findings provide potential optimization techniques and recommend a baseline model for future development. It encompasses a broad range of DNN optimization techniques, including network pruning, weight quantization, knowledge distillation, depthwise separable convolution, residual connections, factorization, dense connections, and compound scaling. Moreover, the review analyzes the established optimization models which utilizes the above optimization techniques. A comprehensive analysis is conducted for each technique and model, considering its specific attributes, usability, strengths, and limitations in the context of effective deployment in RCEs. The review also presents a comparative assessment of advanced DNN models’ deployment for image classification, employing key evaluation metrics such as accuracy and efficiency factors like memory and inference time. The article concludes with the finding that combining depthwise separable convolution, weight quantization, and pruning represents potential optimization techniques, while also recommending EfficientNetB1 as a baseline model for the future development of optimization models in RCE image classification.<p> </p>

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“…A CNN-based leaf identification system has been proposed by Taslim et al [12]. The suggested system can detect five local Malaysian leaf varieties.…”

Section: Literature Reviewmentioning

confidence: 99%

LeafNet: Using Convolutional Neural Network for Plant Leaf Detection

Saeed,

Faiz,

Meghji

2023

VFAST trans. softw. eng.

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Pakistan is home to thousands of plant species. As a result of pollution, natural disasters, and climate change, many of these species are at risk of extinction. Plant categorization and detection systems are designed to assist non-experts in automatically identifying plants based on their leaves to ensure their safety. The current study proposes a plant leaf detection system utilizing a Convolutional Neural Network architecture. Making use of the PlantVillage dataset, the proposed system can identify seven species of plants namely apple, cherry, tomato, potato, soybean, strawberry, and corn. Data augmentation strategies have been used to provide more training examples to get around the problem of bias and imbalanced data. The accuracy achieved on the training set was 98.87% which improved to 99.5% when using data augmentation. Apart from the monitoring of endangered species, the adoption of the proposed model can also aid the evaluation of weed management efforts and analysis of species distribution under climate change.

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Plant leaf identification system using convolutional neural network

Cited by 6 publications

References 21 publications

Malaysia coin identification app using deep learning model

Malaysia coin identification app using deep learning model

Deep neural networks optimization for resource-constrained environments: techniques and models

LeafNet: Using Convolutional Neural Network for Plant Leaf Detection

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