Plant leaf identification system using convolutional neural network
| Autor: | Abd Kadir Mahamad, Sharifah Saon, Muladi Muladi, Wahyu Nur Hidayat, Amiruzzaki Taslim |
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| Rok vydání: | 2021 |
| Předmět: |
Control and Optimization
Contextual image classification Artificial neural network Computer Networks and Communications business.industry Computer science Deep learning Feature extraction Pattern recognition Convolutional neural network Plant identification Identification (information) Hardware and Architecture Control and Systems Engineering Computer Science (miscellaneous) Noise (video) Artificial intelligence Electrical and Electronic Engineering business Instrumentation Information Systems |
| Zdroj: | Bulletin of Electrical Engineering and Informatics. 10:3341-3352 |
| ISSN: | 2302-9285 2089-3191 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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