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- W4387162104 abstract "A large percentage of the world is owned by plants. 82.4% of the biomass of the planet is made up of plants. There are many varieties of plant species: some are edible, some are medicinal, and some are poisonous. Many people could not differentiate between one species from another. This work is aiming to classify a plant on the basis of its structure of leaf from a well-known botanical dataset named Flavia dataset. Our proposed system uses convolutional neural networks (CNN) to classify 1907 leaf images from 32 different species of plants. We evaluate the effectiveness of five CNN models using various performance measures. The experimental results indicate that the method for classification gives an average accuracy of 100% during the training phase and about 99.3% accuracy during testing phase." @default.
- W4387162104 created "2023-09-30" @default.
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- W4387162104 date "2023-06-08" @default.
- W4387162104 modified "2023-09-30" @default.
- W4387162104 title "Analysis of Deep Learning Classification Models for Predicting Plant Species from Single Leaf Images" @default.
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- W4387162104 doi "https://doi.org/10.1109/ic2e357697.2023.10262811" @default.
- W4387162104 hasPublicationYear "2023" @default.
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