CLASSIFICATION OF PLANT LEAVES AND FLOWERS USING IMAGE PROCESSING AND DEEP NEURAL NETWORKS

: Plants have shown an important role for our life and industry. In nature, there exists a large number of species of plants. The recognition and classification of plants is a challenging task. The understanding of plants allows us to develop various useful applications in our life. The paper presents a classification method of plant leaves and flowers using the image processing and deep neural networks (e.g., Alexnet, VGG, Resnet-50). The proposed method has been applied for leaves and flowers images that are collected and normalized from multiple sources. We have evaluated the proposed method on two public datasets: plant leaves and flowers. The large dataset is collected and prepared from various sources. The classification accuracies of 94% and 95% are obtained for the plant leaves and flowers, respectively. The obtained results have shown the effectiveness of our proposed method

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Publication Details

Journal
International Journal of Advanced Research in Computer Science
Published
2024-10-20
DOI
https://doi.org/10.26483/ijarcs.v15i5.7137
Primary Topic
Smart Agriculture and AI
Type
article
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article

CLASSIFICATION OF PLANT LEAVES AND FLOWERS USING IMAGE PROCESSING AND DEEP NEURAL NETWORKS

Bui Hai Phong
International Journal of Advanced Research in Computer Science
Smart Agriculture and AI
article

CLASSIFICATION OF PLANT LEAVES AND FLOWERS USING IMAGE PROCESSING AND DEEP NEURAL NETWORKS

Bui Hai Phong
article en

Abstract

: Plants have shown an important role for our life and industry. In nature, there exists a large number of species of plants. The recognition and classification of plants is a challenging task. The understanding of plants allows us to develop various useful applications in our life. The paper presents a classification method of plant leaves and flowers using the image processing and deep neural networks (e.g., Alexnet, VGG, Resnet-50). The proposed method has been applied for leaves and flowers images that are collected and normalized from multiple sources. We have evaluated the proposed method on two public datasets: plant leaves and flowers. The large dataset is collected and prepared from various sources. The classification accuracies of 94% and 95% are obtained for the plant leaves and flowers, respectively. The obtained results have shown the effectiveness of our proposed method

International Journal of Advanced Research in Computer ScienceVol. 15(5)
Hanoi Architectural University (VN)
Industry, innovation and infrastructure
Openalex Percentile: Top 90%
Smart Agriculture and AI
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