Transfer Learning Approach to Seed Taxonomy: A Wild Plant Case Study

Plant taxonomy is the scientific study of the classification and naming of various plant species. It is a branch of biology that aims to categorize and organize the diverse variety of plant life on earth. Traditionally, plant taxonomy has been performed using morphological and anatomical characteris...

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Bibliographic Details
Published in:Big data and cognitive computing 2023-09, Vol.7 (3), p.128
Main Authors: Ibrahim, Nehad M., Gabr, Dalia G., Rahman, Atta, Musleh, Dhiaa, AlKhulaifi, Dania, AlKharraa, Mariam
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Case studies
Classification
Cluster analysis
Clustering
Computational linguistics
Datasets
Deep learning
DenseNet201
Flowers & plants
Language processing
Machine learning
Natural language interfaces
Neural networks
Plants (botany)
plants taxonomy
Taxonomy
transfer learning
Vector quantization
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Summary:Plant taxonomy is the scientific study of the classification and naming of various plant species. It is a branch of biology that aims to categorize and organize the diverse variety of plant life on earth. Traditionally, plant taxonomy has been performed using morphological and anatomical characteristics, such as leaf shape, flower structure, and seed and fruit characters. Artificial intelligence (AI), machine learning, and especially deep learning can also play an instrumental role in plant taxonomy by automating the process of categorizing plant species based on the available features. This study investigated transfer learning techniques to analyze images of plants and extract features that can be used to cluster the species hierarchically using the k-means clustering algorithm. Several pretrained deep learning models were employed and evaluated. In this regard, two separate datasets were used in the study comprising of seed images of wild plants collected from Egypt. Extensive experiments using the transfer learning method (DenseNet201) demonstrated that the proposed methods achieved superior accuracy compared to traditional methods with the highest accuracy of 93% and F1-score and area under the curve (AUC) of 95%, respectively. That is considerable in contrast to the state-of-the-art approaches in the literature.
ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc7030128