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Paranita Asnur

Universitas Gunadarma

Author-ID : 6871250

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Identification of mangrove tree species using deep learning method Paranita Asnur; Rifki Kosasih; Sarifuddin Madenda; Dewi A. Rahayu
International Journal of Advances in Applied Sciences Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v12.i2.pp163-170

Artificial intelligence can help classify plants to make identification easier for everyone. This technology can be used to classify mangrove trees. The degradation of mangrove forests has resulted in a 20% loss of biodiversity, an 80% loss of microbial decomposers, reduced C-organic soil, and fish spawning grounds, resulting in estimated losses in the ecological and economic fields for up to IDR 39 billion. The identification of different mangrove species is the first step in ensuring the preservation of these forests. Therefore, this research aimed to develop algorithms and a convolutional neural network (CNN) architecture to classify mangrove tree species with the highest possible accuracy using Python software. The architecture selection for this model includes a batch size of 32, an input image size of 128x128 pixels, four classes, four convolution layers, four rectified linear unit (ReLU) layers, 2x2 max-pooling, and two fully connected layers (FCL). The finding showed that the resulting accuracy from the test was 97.50%, while the validation test was 81.25%, applied to four types of mangrove leaves, including Avicenia marina, Avicenia officialis, Rizophora apiculata, and Soneratia caseolaris.

Identification of mangrove tree species using deep learning method Paranita Asnur; Rifki Kosasih; Sarifuddin Madenda; Dewi A. Rahayu
International Journal of Advances in Applied Sciences Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v12.i2.pp163-170

Artificial intelligence can help classify plants to make identification easier for everyone. This technology can be used to classify mangrove trees. The degradation of mangrove forests has resulted in a 20% loss of biodiversity, an 80% loss of microbial decomposers, reduced C-organic soil, and fish spawning grounds, resulting in estimated losses in the ecological and economic fields for up to IDR 39 billion. The identification of different mangrove species is the first step in ensuring the preservation of these forests. Therefore, this research aimed to develop algorithms and a convolutional neural network (CNN) architecture to classify mangrove tree species with the highest possible accuracy using Python software. The architecture selection for this model includes a batch size of 32, an input image size of 128x128 pixels, four classes, four convolution layers, four rectified linear unit (ReLU) layers, 2x2 max-pooling, and two fully connected layers (FCL). The finding showed that the resulting accuracy from the test was 97.50%, while the validation test was 81.25%, applied to four types of mangrove leaves, including Avicenia marina, Avicenia officialis, Rizophora apiculata, and Soneratia caseolaris.

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Journal : International Journal of Advances in Applied Sciences