Deep Learning to Identify Plant Species

Fayas Rasheed , Liz George

Index Terms: Deep learning, Species, Architecture (key words)

Abstract: Deep learning is the method that has the ability to develop precise models for recognizing images. Deep learning has shown potential for automating the process of identifying various plant species from images of their leaves, flowers, and fruits. To reduce noise and improve the plant features, the input images undergo pre-processing. The deep learning model is then trained using a sizable labelled dataset of plant images. Once trained, the model can accurately recognize the plant species from new images. While automated plant classification systems typically rely on leaf shape as the main feature for identification, leaves also have other characteristics that can contribute to more precise classification, such as their texture, vein patterns, and color. This technology has the potential to be applied in various fields, such as agriculture, botany, and environmental conservation, to help identify and monitor different plant species in their natural surroundings. However, as with any deep learning application, the quality of the training data and the neural network architecture design are essential factors that can have a significant impact on the system's performance.

Published Paper   E-Certificate

"Deep Learning to Identify Plant Species", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 3, page no.1444 - 1447, March-2023, Available :https://ijsdr.org/papers/IJSDR2303249.pdf

Volume 8 Issue 3, March-2023

Pages : 1444 - 1447

Paper Reg. ID: IJSDR_204977

Published Paper Id: IJSDR2303249

Downloads: 000347465

Research Area: Engineering

Country: -, -, -

Published Paper PDF: https://ijsdr.org/papers/IJSDR2303249

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2303249

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

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