Plant species identification from leaf images using deep learning models (cnn-lstm architecture)
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Tanzania Journal of Forestry and Nature Conservation
Abstract
Species knowledge is important for
biodiversity conservation. Identification of
plants by conventional approach is complex,
time consuming, and frustrating tor non-
experts due to the use of botanical terms.
This is a challenge for learners interested in
acquiring species knowledge. Recently, an
interest has surfaced in automating the
process of species identification. The
combined availability and ubiquity of
relevant technologies, such as digital
cameras and mobile devices, advanced
techniques in image processing and pattern
recognition makes the idea of automated
species identification become real. This
paper
elucidates
development
of
convolutional neural network models to
perform plant species identification using
simple leaves images of plants, through deep
learning methodologies. Training of the
models was performed by using an open
database of 100 plant species images,
containing 64 different element vectors of
plants in a set of 100 distinct classes of plant
species. Several state-of the- art model
architectures were trained, with the proposed
model attaining performance of 95.06%
success rate in identifying the corresponding
plant species. The significant success rate
makes the model very useful identifier or/and
advisory tool. The approach could be further
expanded to support an integrated plant
species identification system to operate in
real ecosystem services.
Description
Journal Article
Keywords
Biodiversity, Computer vision, Convolutional neural networks, Plant species, Deep learning